{"id":77890,"date":"2024-09-24T23:46:56","date_gmt":"2024-09-24T20:16:56","guid":{"rendered":"https:\/\/nabfollower.com\/blog\/ml-model-selection-1437\/"},"modified":"2024-09-24T23:46:56","modified_gmt":"2024-09-24T20:16:56","slug":"ml-model-selection-1437","status":"publish","type":"post","link":"https:\/\/nabfollower.com\/blog\/ml-model-selection-1437\/","title":{"rendered":"\u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 ML &#8211; \u0627\u0646\u062c\u0645\u0646 DEV"},"content":{"rendered":"<p>Summarize this content to 400 words in Persian Lang <\/p>\n<p>  1. \u0645\u0642\u062f\u0645\u0647<\/p>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u06cc\u0627\u062f \u0645\u06cc\u200c\u06af\u06cc\u0631\u06cc\u0645 \u06a9\u0647 \u0686\u06af\u0648\u0646\u0647 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0631\u0627 \u0628\u06cc\u0646 \u0686\u0646\u062f\u06cc\u0646 \u0645\u062f\u0644 \u0628\u0627 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u0646\u06cc\u0645\u060c \u062f\u0631 \u0628\u0631\u062e\u06cc \u0645\u0648\u0627\u0631\u062f \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0628\u06cc\u0634 \u0627\u0632 50 \u0645\u062f\u0644 \u0645\u062e\u062a\u0644\u0641 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u0645\u060c \u062f\u0627\u0646\u0633\u062a\u0646 \u0646\u062d\u0648\u0647 \u0627\u0646\u062a\u062e\u0627\u0628 \u06cc\u06a9\u06cc \u0628\u0631\u0627\u06cc \u0628\u0647 \u062f\u0633\u062a \u0622\u0648\u0631\u062f\u0646 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0639\u0645\u0644\u06a9\u0631\u062f \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0634\u0645\u0627 \u0645\u0647\u0645 \u0627\u0633\u062a.<\/p>\n<p>\u0645\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 \u0631\u0627 \u0647\u0645 \u0628\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0648 \u0647\u0645 \u0628\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0647\u0627\u06cc\u067e\u0631\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0622\u0646 \u0627\u0646\u062c\u0627\u0645 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0627\u062f.<\/p>\n<p>\u0627\u0645\u0627 \u0627\u0648\u0644 \u0686\u0647 \u0647\u0633\u062a\u0646\u062f \u0647\u0627\u06cc\u067e\u0631\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627? \u0627\u06cc\u0646\u0647\u0627 \u062a\u0646\u0638\u06cc\u0645\u0627\u062a \u0627\u0636\u0627\u0641\u06cc \u0647\u0633\u062a\u0646\u062f \u06a9\u0647 \u062a\u0648\u0633\u0637 \u06a9\u0627\u0631\u0628\u0631 \u062a\u0646\u0638\u06cc\u0645 \u0645\u06cc \u0634\u0648\u0646\u062f \u0648 \u0628\u0631 \u0646\u062d\u0648\u0647 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u062f\u0644 \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0622\u0646 \u062a\u0623\u062b\u06cc\u0631 \u0645\u06cc \u06af\u0630\u0627\u0631\u0646\u062f. \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627 \u0627\u0632 \u0633\u0648\u06cc \u062f\u06cc\u06af\u0631\u060c \u0645\u062f\u0644 \u0647\u0627 \u062f\u0631 \u0637\u0648\u0644 \u0641\u0631\u0622\u06cc\u0646\u062f \u0622\u0645\u0648\u0632\u0634 \u06cc\u0627\u062f \u0645\u06cc \u06af\u06cc\u0631\u0646\u062f.<\/p>\n<p>  2. \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062c\u0633\u062a\u062c\u0648\u06cc \u062c\u0627\u0645\u0639.<\/p>\n<p>\u062c\u0633\u062a\u062c\u0648\u06cc \u062c\u0627\u0645\u0639 \u0634\u0627\u0645\u0644 \u0627\u0646\u062a\u062e\u0627\u0628 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0628\u0627 \u062c\u0633\u062a\u062c\u0648 \u062f\u0631 \u0637\u06cc\u0641 \u0648\u0633\u06cc\u0639\u06cc \u0627\u0632 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627 \u0627\u0633\u062a. \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0645\u0627 \u0627\u0632 scikit-learn \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645 GridSearchCV.<\/p>\n<p>GridSearchCV \u0686\u06af\u0648\u0646\u0647 \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<p>\u06a9\u0627\u0631\u0628\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u0627\u06cc \u0627\u0632 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0645\u0645\u06a9\u0646 \u0631\u0627 \u0628\u0631\u0627\u06cc \u06cc\u06a9 \u06cc\u0627 \u0686\u0646\u062f \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u062a\u0639\u0631\u06cc\u0641 \u0645\u06cc \u06a9\u0646\u062f.<br \/>\nGridSearchCV \u06cc\u06a9 \u0645\u062f\u0644 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0647\u0631 \u0645\u0642\u062f\u0627\u0631 \u0648 \/ \u06cc\u0627 \u062a\u0631\u06a9\u06cc\u0628\u06cc \u0627\u0632 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0622\u0645\u0648\u0632\u0634 \u0645\u06cc \u062f\u0647\u062f.<br \/>\n\u0645\u062f\u0644 \u0628\u0627 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0639\u0645\u0644\u06a9\u0631\u062f \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<p>\u0645\u062b\u0627\u0644\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u06cc\u06a9 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0644\u062c\u0633\u062a\u06cc\u06a9 \u0631\u0627 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062e\u0648\u062f \u062a\u0646\u0638\u06cc\u0645 \u06a9\u0646\u06cc\u0645 \u0648 \u062f\u0648 \u0627\u0628\u0631\u067e\u0627\u0631\u0627\u0645\u062a\u0631 (C \u0648 \u062c\u0631\u06cc\u0645\u0647 \u0645\u0646\u0638\u0645 \u0633\u0627\u0632\u06cc) \u0631\u0627 \u062a\u0646\u0638\u06cc\u0645 \u06a9\u0646\u06cc\u0645. \u0647\u0645\u0686\u0646\u06cc\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u062f\u0648 \u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u062a\u06a9\u0631\u0627\u0631 \u062d\u0644 \u06a9\u0646\u0646\u062f\u0647 \u0648 \u062d\u062f\u0627\u06a9\u062b\u0631 \u0631\u0627 \u0645\u0634\u062e\u0635 \u06a9\u0646\u06cc\u0645.<\/p>\n<p>\u0627\u06a9\u0646\u0648\u0646 \u0628\u0631\u0627\u06cc \u0647\u0631 \u062a\u0631\u06a9\u06cc\u0628\u06cc \u0627\u0632 \u0645\u0642\u0627\u062f\u06cc\u0631 \u062c\u0631\u06cc\u0645\u0647 C \u0648 \u0645\u0646\u0638\u0645 \u0633\u0627\u0632\u06cc\u060c \u0645\u062f\u0644 \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u0645\u06cc \u062f\u0647\u06cc\u0645 \u0648 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0627\u0639\u062a\u0628\u0627\u0631\u0633\u0646\u062c\u06cc \u0645\u062a\u0642\u0627\u0637\u0639 k-fold \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u0645.\u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0645\u0627 10 \u0645\u0642\u062f\u0627\u0631 \u0645\u0645\u06a9\u0646 \u0628\u0631\u0627\u06cc C \u062f\u0627\u0631\u06cc\u0645\u060c 2 \u0645\u0642\u062f\u0627\u0631 \u0645\u0645\u06a9\u0646 \u0628\u0631\u0627\u06cc reg. \u067e\u0646\u0627\u0644\u062a\u06cc \u0648 5 \u0628\u0631\u0627\u0628\u0631 \u0645\u0627 \u062f\u0631 \u0645\u062c\u0645\u0648\u0639 (10*2*5 = 100) \u0645\u062f\u0644\u0647\u0627\u06cc \u06a9\u0627\u0646\u062f\u06cc\u062f \u062f\u0627\u0631\u06cc\u0645 \u06a9\u0647 \u0627\u0632 \u0628\u06cc\u0646 \u0622\u0646\u0647\u0627 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<p># Load libraries<br \/>\nimport numpy as np<br \/>\nfrom sklearn import linear_model, datasets<br \/>\nfrom sklearn.model_selection import GridSearchCV<\/p>\n<p># Load data<br \/>\niris = datasets.load_iris()<br \/>\nfeatures = iris.data<br \/>\ntarget = iris.target<\/p>\n<p># Create logistic regression<br \/>\nlogistic = linear_model.LogisticRegression(max_iter=500, solver=&#8221;liblinear&#8221;)<\/p>\n<p># Create range of candidate penalty hyperparameter values<br \/>\npenalty = [&#8216;l1&#8242;,&#8217;l2&#8242;]\n<p># Create range of candidate regularization hyperparameter values<br \/>\nC = np.logspace(0, 4, 10)<\/p>\n<p># Create dictionary of hyperparameter candidates<br \/>\nhyperparameters = dict(C=C, penalty=penalty)<\/p>\n<p># Create grid search<br \/>\ngridsearch = GridSearchCV(logistic, hyperparameters, cv=5, verbose=0)<\/p>\n<p># Fit grid search<br \/>\nbest_model = gridsearch.fit(features, target)<\/p>\n<p># Show the best model<br \/>\nprint(best_model.best_estimator_)<\/p>\n<p># LogisticRegression(C=7.742636826811269, max_iter=500, penalty=&#8217;l1&#8217;,<br \/>\nsolver=&#8221;liblinear&#8221;) # Result<\/p>\n<p>    \u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/p>\n<p>    \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/p>\n<p>\u06af\u0631\u0641\u062a\u0646 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644:<\/p>\n<p># View best hyperparameters<br \/>\nprint(&#8216;Best Penalty:&#8217;, best_model.best_estimator_.get_params()[&#8216;penalty&#8217;])<br \/>\nprint(&#8216;Best C:&#8217;, best_model.best_estimator_.get_params()[&#8216;C&#8217;])<\/p>\n<p># Best Penalty: l1 #Result<br \/>\n# Best C: 7.742636826811269 # Result<\/p>\n<p>    \u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/p>\n<p>    \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/p>\n<p>  3. \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062c\u0633\u062a\u062c\u0648\u06cc \u062a\u0635\u0627\u062f\u0641\u06cc.<\/p>\n<p>\u0627\u06cc\u0646 \u0645\u0639\u0645\u0648\u0644\u0627\u064b \u0632\u0645\u0627\u0646\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f \u06a9\u0647 \u0628\u0631\u0627\u06cc \u0627\u0646\u062a\u062e\u0627\u0628 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644\u060c \u0631\u0648\u0634 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0627\u0631\u0632\u0627\u0646\u200c\u062a\u0631\u06cc \u0646\u0633\u0628\u062a \u0628\u0647 \u062c\u0633\u062a\u062c\u0648\u06cc \u062c\u0627\u0645\u0639 \u0645\u06cc\u200c\u062e\u0648\u0627\u0647\u06cc\u062f.<\/p>\n<p>\u0634\u0627\u06cc\u0627\u0646 \u0630\u06a9\u0631 \u0627\u0633\u062a \u06a9\u0647 \u062f\u0644\u06cc\u0644 \u0627\u06cc\u0646\u06a9\u0647 RandomizedSearchCV \u0630\u0627\u062a\u0627\u064b \u0633\u0631\u06cc\u0639\u062a\u0631 \u0627\u0632 GridSearchCV \u0646\u06cc\u0633\u062a\u060c \u0627\u0645\u0627 \u0627\u063a\u0644\u0628 \u0628\u0627 \u0622\u0632\u0645\u0627\u06cc\u0634 \u062a\u0631\u06a9\u06cc\u0628\u0627\u062a \u06a9\u0645\u062a\u0631\u060c \u0639\u0645\u0644\u06a9\u0631\u062f\u06cc \u0642\u0627\u0628\u0644 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0628\u0627 GridSearchCV \u062f\u0631 \u0632\u0645\u0627\u0646 \u06a9\u0645\u062a\u0631\u06cc \u062f\u0627\u0631\u062f.<\/p>\n<p>\u0646\u062d\u0648\u0647 \u06a9\u0627\u0631\u06a9\u0631\u062f \u062a\u0635\u0627\u062f\u0641\u06cc \u062c\u0633\u062a\u062c\u0648\u06cc CV:<\/p>\n<p>\u06a9\u0627\u0631\u0628\u0631 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\/\u062a\u0648\u0632\u06cc\u0639\u0627\u062a (\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644 \u0646\u0631\u0645\u0627\u0644\u060c \u06cc\u06a9\u0646\u0648\u0627\u062e\u062a) \u0631\u0627 \u0639\u0631\u0636\u0647 \u0645\u06cc \u06a9\u0646\u062f.<br \/>\n\u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645\u200c\u0647\u0627 \u0628\u0647\u200c\u0637\u0648\u0631 \u062a\u0635\u0627\u062f\u0641\u06cc \u062a\u0639\u062f\u0627\u062f \u062e\u0627\u0635\u06cc \u0627\u0632 \u062a\u0631\u06a9\u06cc\u0628\u200c\u0647\u0627\u06cc \u062a\u0635\u0627\u062f\u0641\u06cc \u0645\u0642\u0627\u062f\u06cc\u0631 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u062f\u0627\u062f\u0647\u200c\u0634\u062f\u0647 \u0631\u0627 \u0628\u062f\u0648\u0646 \u062c\u0627\u06cc\u06af\u0632\u06cc\u0646\u06cc \u062c\u0633\u062a\u062c\u0648 \u0645\u06cc\u200c\u06a9\u0646\u0646\u062f.<\/p>\n<p>\u0645\u062b\u0627\u0644<\/p>\n<p># Load data<br \/>\niris = datasets.load_iris()<br \/>\nfeatures = iris.data<br \/>\ntarget = iris.target<\/p>\n<p># Create logistic regression<br \/>\nlogistic = linear_model.LogisticRegression(max_iter=500, solver=&#8221;liblinear&#8221;)<\/p>\n<p># Create range of candidate regularization penalty hyperparameter values<br \/>\npenalty = [&#8216;l1&#8217;, &#8216;l2&#8242;]\n<p># Create distribution of candidate regularization hyperparameter values<br \/>\nC = uniform(loc=0, scale=4)<\/p>\n<p># Create hyperparameter options<br \/>\nhyperparameters = dict(C=C, penalty=penalty)<\/p>\n<p># Create randomized search<br \/>\nrandomizedsearch = RandomizedSearchCV(<br \/>\nlogistic, hyperparameters, random_state=1, n_iter=100, cv=5, verbose=0,<br \/>\nn_jobs=-1)<\/p>\n<p># Fit randomized search<br \/>\nbest_model = randomizedsearch.fit(features, target)<\/p>\n<p># Print best model<br \/>\nprint(best_model.best_estimator_)<\/p>\n<p># LogisticRegression(C=1.668088018810296, max_iter=500, penalty=&#8217;l1&#8217;,<br \/>\nsolver=&#8221;liblinear&#8221;) #Result.<\/p>\n<p>    \u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/p>\n<p>    \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/p>\n<p>\u062f\u0631\u06cc\u0627\u0641\u062a \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644:<\/p>\n<p># View best hyperparameters<br \/>\nprint(&#8216;Best Penalty:&#8217;, best_model.best_estimator_.get_params()[&#8216;penalty&#8217;])<br \/>\nprint(&#8216;Best C:&#8217;, best_model.best_estimator_.get_params()[&#8216;C&#8217;])<\/p>\n<p># Best Penalty: l1 # Result<br \/>\n# Best C: 1.668088018810296 # Result<\/p>\n<p>    \u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/p>\n<p>    \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/p>\n<p>\u062a\u0648\u062c\u0647: \u062a\u0639\u062f\u0627\u062f \u0645\u062f\u0644 \u0647\u0627\u06cc \u06a9\u0627\u0646\u062f\u06cc\u062f \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647 \u062f\u0631 \u0645\u0634\u062e\u0635 \u0634\u062f\u0647 \u0627\u0633\u062a n_iter \u062a\u0646\u0638\u06cc\u0645\u0627\u062a (\u062a\u0639\u062f\u0627\u062f \u062a\u06a9\u0631\u0627\u0631).<\/p>\n<p>  4. \u0627\u0646\u062a\u062e\u0627\u0628 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0647\u0627 \u0627\u0632 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0686\u0646\u062f\u06af\u0627\u0646\u0647.<\/p>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0628\u062e\u0634 \u0628\u0647 \u0646\u062d\u0648\u0647 \u0627\u0646\u062a\u062e\u0627\u0628 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0628\u0627 \u062c\u0633\u062a\u062c\u0648 \u062f\u0631 \u0637\u06cc\u0641 \u0648\u0633\u06cc\u0639\u06cc \u0627\u0632 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645\u200c\u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0648 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0645\u0631\u0628\u0648\u0637\u0647 \u0645\u06cc\u200c\u067e\u0631\u062f\u0627\u0632\u06cc\u0645.<\/p>\n<p>\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0631\u0627 \u0628\u0647 \u0633\u0627\u062f\u06af\u06cc \u0628\u0627 \u0627\u06cc\u062c\u0627\u062f \u0641\u0631\u0647\u0646\u06af \u0644\u063a\u062a \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0646\u0627\u0645\u0632\u062f \u0648 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0622\u0646\u0647\u0627 \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u0645 \u062a\u0627 \u0627\u0632 \u0622\u0646\u0647\u0627 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0641\u0636\u0627\u06cc \u062c\u0633\u062a\u062c\u0648 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645. GridSearchCV.<\/p>\n<p>\u0645\u0631\u0627\u062d\u0644:<\/p>\n<p>\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0641\u0636\u0627\u06cc \u062c\u0633\u062a\u062c\u0648\u06cc\u06cc \u0631\u0627 \u062a\u0639\u0631\u06cc\u0641 \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u0634\u0627\u0645\u0644 \u062f\u0648 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0627\u0633\u062a.<br \/>\n\u0645\u0627 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627 \u0631\u0627 \u0645\u0634\u062e\u0635 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u0648 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0642\u0627\u0644\u0628\u060c \u0645\u0642\u0627\u062f\u06cc\u0631 \u06a9\u0627\u0646\u062f\u06cc\u062f \u0622\u0646\u0647\u0627 \u0631\u0627 \u062a\u0639\u0631\u06cc\u0641 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u06a9\u0646\u0646\u062f\u0647[hyperparameter name]_.<\/p>\n<p># Load libraries<br \/>\nimport numpy as np<br \/>\nfrom sklearn import datasets<br \/>\nfrom sklearn.linear_model import LogisticRegression<br \/>\nfrom sklearn.ensemble import RandomForestClassifier<br \/>\nfrom sklearn.model_selection import GridSearchCV<br \/>\nfrom sklearn.pipeline import Pipeline<\/p>\n<p># Set random seed<br \/>\nnp.random.seed(0)<\/p>\n<p># Load data<br \/>\niris = datasets.load_iris()<br \/>\nfeatures = iris.data<br \/>\ntarget = iris.target<\/p>\n<p># Create a pipeline<br \/>\npipe = Pipeline([(&#8220;classifier&#8221;, RandomForestClassifier())])<\/p>\n<p># Create dictionary with candidate learning algorithms and their hyperparameters<br \/>\nsearch_space = [{&#8220;classifier&#8221;: [LogisticRegression(max_iter=500,<br \/>\nsolver=&#8221;liblinear&#8221;)],<br \/>\n&#8220;classifier__penalty&#8221;: [&#8216;l1&#8217;, &#8216;l2&#8217;],<br \/>\n&#8220;classifier__C&#8221;: np.logspace(0, 4, 10)},<br \/>\n{&#8220;classifier&#8221;: [RandomForestClassifier()],<br \/>\n&#8220;classifier__n_estimators&#8221;: [10, 100, 1000],<br \/>\n&#8220;classifier__max_features&#8221;: [1, 2, 3]}]\n<p># Create grid search<br \/>\ngridsearch = GridSearchCV(pipe, search_space, cv=5, verbose=0)<\/p>\n<p># Fit grid search<br \/>\nbest_model = gridsearch.fit(features, target)<\/p>\n<p># Print best model<br \/>\nprint(best_model.best_estimator_)<\/p>\n<p># Pipeline(steps=[(&#8216;classifier&#8217;,<br \/>\n                 LogisticRegression(C=7.742636826811269, max_iter=500,<br \/>\n                      penalty=&#8217;l1&#8242;, solver=&#8221;liblinear&#8221;))])<\/p>\n<p>    \u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/p>\n<p>    \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/p>\n<p>\u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644:\u067e\u0633 \u0627\u0632 \u0627\u062a\u0645\u0627\u0645 \u062c\u0633\u062a\u062c\u0648 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645 best_estimator_ \u0628\u0631\u0627\u06cc \u0645\u0634\u0627\u0647\u062f\u0647 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0648 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0645\u062f\u0644.<\/p>\n<p>  5. \u0627\u0646\u062a\u062e\u0627\u0628 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0647\u0646\u06af\u0627\u0645 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634.<\/p>\n<p>\u06af\u0627\u0647\u06cc \u0627\u0648\u0642\u0627\u062a \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0628\u062e\u0648\u0627\u0647\u06cc\u0645 \u06cc\u06a9 \u0645\u0631\u062d\u0644\u0647 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0631\u0627 \u062f\u0631 \u0637\u0648\u0644 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 \u0644\u062d\u0627\u0638 \u06a9\u0646\u06cc\u0645.\u0628\u0647\u062a\u0631\u06cc\u0646 \u0631\u0627\u0647 \u062d\u0644 \u0627\u06cc\u062c\u0627\u062f \u062e\u0637 \u0644\u0648\u0644\u0647 \u0627\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0634\u0627\u0645\u0644 \u0645\u0631\u062d\u0644\u0647 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0648 \u0647\u0631 \u06cc\u06a9 \u0627\u0632 \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0622\u0646 \u0628\u0627\u0634\u062f:<\/p>\n<p>\u0686\u0627\u0644\u0634 \u0627\u0648\u0644:GridSeachCv \u0627\u0632 \u0627\u0639\u062a\u0628\u0627\u0631\u0633\u0646\u062c\u06cc \u0645\u062a\u0642\u0627\u0628\u0644 \u0628\u0631\u0627\u06cc \u062a\u0639\u06cc\u06cc\u0646 \u0645\u062f\u0644 \u0628\u0627 \u0628\u0627\u0644\u0627\u062a\u0631\u06cc\u0646 \u0639\u0645\u0644\u06a9\u0631\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<p>\u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u062f\u0631 \u0627\u0639\u062a\u0628\u0627\u0631\u0633\u0646\u062c\u06cc \u0645\u062a\u0642\u0627\u0637\u0639\u060c \u0645\u0627 \u0648\u0627\u0646\u0645\u0648\u062f \u0645\u06cc\u200c\u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u0686\u06cc\u0646 \u0628\u0647\u200c\u0639\u0646\u0648\u0627\u0646 \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u062f\u06cc\u062f\u0647 \u0646\u0645\u06cc\u200c\u0634\u0648\u062f\u060c \u0648 \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0628\u062e\u0634\u06cc \u0627\u0632 \u0628\u0631\u0627\u0632\u0634 \u0647\u0631 \u0645\u0631\u062d\u0644\u0647 \u067e\u06cc\u0634\u200c\u067e\u0631\u062f\u0627\u0632\u0634 (\u0645\u0627\u0646\u0646\u062f \u0645\u0642\u06cc\u0627\u0633\u200c\u0628\u0646\u062f\u06cc \u06cc\u0627 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f\u0633\u0627\u0632\u06cc) \u0646\u06cc\u0633\u062a.<\/p>\n<p>\u0628\u0647 \u0647\u0645\u06cc\u0646 \u062f\u0644\u06cc\u0644 \u0645\u0631\u0627\u062d\u0644 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0628\u0627\u06cc\u062f \u0628\u062e\u0634\u06cc \u0627\u0632 \u0645\u062c\u0645\u0648\u0639\u0647 \u0627\u0642\u062f\u0627\u0645\u0627\u062a \u0627\u0646\u062c\u0627\u0645 \u0634\u062f\u0647 \u062a\u0648\u0633\u0637 GridSearchCV \u0628\u0627\u0634\u062f.<\/p>\n<p>\u0631\u0627\u0647 \u062d\u0644Scikit-learn \u0641\u0631\u0627\u0647\u0645 \u0645\u06cc \u06a9\u0646\u062f FeatureUnion \u06a9\u0647 \u0628\u0647 \u0645\u0627 \u0627\u0645\u06a9\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u0686\u0646\u062f\u06cc\u0646 \u0639\u0645\u0644\u06cc\u0627\u062a \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0631\u0627 \u0628\u0647 \u062f\u0631\u0633\u062a\u06cc \u062a\u0631\u06a9\u06cc\u0628 \u06a9\u0646\u06cc\u0645.\u0645\u0631\u0627\u062d\u0644:<\/p>\n<p>\u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645 _ FeatureUnion _\u0628\u0631\u0627\u06cc \u062a\u0631\u06a9\u06cc\u0628 \u062f\u0648 \u0645\u0631\u062d\u0644\u0647 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634: \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u06a9\u0631\u062f\u0646 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0648\u06cc\u0698\u06af\u06cc (StandardScaler) \u0648 \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0645\u0624\u0644\u0641\u0647 \u0647\u0627\u06cc \u0627\u0635\u0644\u06cc (PCA) &#8211; \u0627\u06cc\u0646 \u0634\u06cc \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0646\u0627\u0645\u06cc\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f \u0648 \u0634\u0627\u0645\u0644 \u0647\u0631 \u062f\u0648 \u0645\u0631\u062d\u0644\u0647 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0645\u0627 \u0627\u0633\u062a.<br \/>\n\u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f\u060c \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0631\u0627 \u0628\u0627 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062e\u0648\u062f \u062f\u0631 \u062e\u0637 \u0644\u0648\u0644\u0647 \u062e\u0648\u062f \u0642\u0631\u0627\u0631 \u0645\u06cc \u062f\u0647\u06cc\u0645.<\/p>\n<p>\u0627\u06cc\u0646 \u0628\u0647 \u0645\u0627 \u0627\u0645\u06a9\u0627\u0646 \u0645\u06cc\u200c\u062f\u0647\u062f \u062a\u0627 \u0645\u062f\u06cc\u0631\u06cc\u062a \u0645\u0646\u0627\u0633\u0628 \u0628\u0631\u0627\u0632\u0634\u060c \u062a\u0628\u062f\u06cc\u0644 \u0648 \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644\u200c\u0647\u0627 \u0631\u0627 \u0628\u0627 \u062a\u0631\u06a9\u06cc\u0628\u06cc \u0627\u0632 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627 \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc scikit \u0628\u0631\u0648\u0646 \u0633\u067e\u0627\u0631\u06cc \u06a9\u0646\u06cc\u0645.<\/p>\n<p>\u0686\u0627\u0644\u0634 \u062f\u0648\u0645:\u0628\u0631\u062e\u06cc \u0627\u0632 \u0631\u0648\u0634\u200c\u0647\u0627\u06cc \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0645\u0627\u0646\u0646\u062f PCA \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u062e\u0627\u0635 \u062e\u0648\u062f \u0631\u0627 \u062f\u0627\u0631\u0646\u062f\u060c \u06a9\u0627\u0647\u0634 \u0627\u0628\u0639\u0627\u062f \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 PCA \u0628\u0647 \u06a9\u0627\u0631\u0628\u0631 \u0646\u06cc\u0627\u0632 \u062f\u0627\u0631\u062f \u06a9\u0647 \u062a\u0639\u062f\u0627\u062f \u0627\u062c\u0632\u0627\u06cc \u0627\u0635\u0644\u06cc \u0631\u0627 \u0628\u0631\u0627\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0628\u0631\u0627\u06cc \u062a\u0648\u0644\u06cc\u062f \u0645\u062c\u0645\u0648\u0639\u0647 \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627\u06cc \u062a\u0628\u062f\u06cc\u0644\u200c\u0634\u062f\u0647 \u062a\u0639\u0631\u06cc\u0641 \u06a9\u0646\u062f. \u062f\u0631 \u062d\u0627\u0644\u062a \u0627\u06cc\u062f\u0647\u200c\u0622\u0644\u060c \u062a\u0639\u062f\u0627\u062f \u0645\u0624\u0644\u0641\u0647\u200c\u0647\u0627\u06cc\u06cc \u0631\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u06cc\u200c\u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u0645\u062f\u0644\u06cc \u0628\u0627 \u0628\u06cc\u0634\u062a\u0631\u06cc\u0646 \u0639\u0645\u0644\u06a9\u0631\u062f \u0631\u0627 \u0628\u0631\u0627\u06cc \u0628\u0631\u062e\u06cc \u0627\u0632 \u0645\u0639\u06cc\u0627\u0631\u0647\u0627\u06cc \u0622\u0632\u0645\u0648\u0646 \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u062a\u0648\u0644\u06cc\u062f \u0645\u06cc\u200c\u06a9\u0646\u0646\u062f.\u0631\u0627\u0647 \u062d\u0644.\u062f\u0631 scikit-learn \u0648\u0642\u062a\u06cc \u0645\u0642\u0627\u062f\u06cc\u0631 \u0645\u0624\u0644\u0641\u0647\u200c\u0647\u0627\u06cc \u0646\u0627\u0645\u0632\u062f \u0631\u0627 \u062f\u0631 \u0641\u0636\u0627\u06cc \u062c\u0633\u062a\u062c\u0648 \u0642\u0631\u0627\u0631 \u0645\u06cc\u200c\u062f\u0647\u06cc\u0645\u060c \u0645\u0627\u0646\u0646\u062f \u0647\u0631 \u0627\u0628\u0631\u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u062f\u06cc\u06af\u0631\u06cc \u06a9\u0647 \u0628\u0627\u06cc\u062f \u062c\u0633\u062a\u062c\u0648 \u0634\u0648\u062f\u060c \u0631\u0641\u062a\u0627\u0631 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/p>\n<p># Load libraries<br \/>\nimport numpy as np<br \/>\nfrom sklearn import datasets<br \/>\nfrom sklearn.linear_model import LogisticRegression<br \/>\nfrom sklearn.model_selection import GridSearchCV<br \/>\nfrom sklearn.pipeline import Pipeline, FeatureUnion<br \/>\nfrom sklearn.decomposition import PCA<br \/>\nfrom sklearn.preprocessing import StandardScaler<\/p>\n<p># Set random seed<br \/>\nnp.random.seed(0)<\/p>\n<p># Load data<br \/>\niris = datasets.load_iris()<br \/>\nfeatures = iris.data<br \/>\ntarget = iris.target<\/p>\n<p># Create a preprocessing object that includes StandardScaler features and PCA<br \/>\npreprocess = FeatureUnion([(&#8220;std&#8221;, StandardScaler()), (&#8220;pca&#8221;, PCA())])<\/p>\n<p># Create a pipeline<br \/>\npipe = Pipeline([(&#8220;preprocess&#8221;, preprocess),<br \/>\n               (&#8220;classifier&#8221;, LogisticRegression(max_iter=1000,<br \/>\n               solver=&#8221;liblinear&#8221;))])<\/p>\n<p># Create space of candidate values<br \/>\nsearch_space = [{&#8220;preprocess__pca__n_components&#8221;: [1, 2, 3],<br \/>\n&#8220;classifier__penalty&#8221;: [&#8220;l1&#8221;, &#8220;l2&#8221;],<br \/>\n&#8220;classifier__C&#8221;: np.logspace(0, 4, 10)}]\n<p># Create grid search<br \/>\nclf = GridSearchCV(pipe, search_space, cv=5, verbose=0, n_jobs=-1)<\/p>\n<p># Fit grid search<br \/>\nbest_model = clf.fit(features, target)<\/p>\n<p># Print best model<br \/>\nprint(best_model.best_estimator_)<\/p>\n<p># Pipeline(steps=[(&#8216;preprocess&#8217;,<br \/>\n     FeatureUnion(transformer_list=[(&#8216;std&#8217;, StandardScaler()),<br \/>\n                                    (&#8216;pca&#8217;, PCA(n_components=1))])),<br \/>\n    (&#8216;classifier&#8217;,<br \/>\n    LogisticRegression(C=7.742636826811269, max_iter=1000,<br \/>\n                      penalty=&#8217;l1&#8242;, solver=&#8221;liblinear&#8221;))]) # Result<\/p>\n<p>    \u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/p>\n<p>    \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/p>\n<p>\u0628\u0639\u062f \u0627\u0632 \u0627\u06cc\u0646\u06a9\u0647 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 \u06a9\u0627\u0645\u0644 \u0634\u062f\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0645\u0642\u0627\u062f\u06cc\u0631 \u067e\u06cc\u0634\u200c\u067e\u0631\u062f\u0627\u0632\u0634 \u0631\u0627 \u06a9\u0647 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0631\u0627 \u062a\u0648\u0644\u06cc\u062f \u06a9\u0631\u062f\u0647\u200c\u0627\u0646\u062f\u060c \u0645\u0634\u0627\u0647\u062f\u0647 \u06a9\u0646\u06cc\u0645.<\/p>\n<p>\u0645\u0631\u0627\u062d\u0644 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u06a9\u0647 \u0628\u0647\u062a\u0631\u06cc\u0646 \u062d\u0627\u0644\u062a \u0647\u0627 \u0631\u0627 \u062a\u0648\u0644\u06cc\u062f \u06a9\u0631\u062f<\/p>\n<p># View best n_components<\/p>\n<p>best_model.best_estimator_.get_params()<br \/>\n# [&#8216;preprocess__pca__n_components&#8217;] # Results<\/p>\n<p>    \u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/p>\n<p>    \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/p>\n<p>  5. \u0627\u0641\u0632\u0627\u06cc\u0634 \u0633\u0631\u0639\u062a \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 \u0628\u0627 \u0645\u0648\u0627\u0632\u06cc \u0633\u0627\u0632\u06cc.<\/p>\n<p>\u062f\u0631 \u0622\u0646 \u0632\u0645\u0627\u0646 \u0628\u0627\u06cc\u062f \u0632\u0645\u0627\u0646 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 \u0631\u0627 \u06a9\u0627\u0647\u0634 \u062f\u0647\u06cc\u062f.\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0631\u0627 \u0628\u0627 \u0622\u0645\u0648\u0632\u0634 \u0686\u0646\u062f\u06cc\u0646 \u0645\u062f\u0644 \u0628\u0647 \u0637\u0648\u0631 \u0647\u0645\u0632\u0645\u0627\u0646 \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u0645\u060c \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0645\u0627\u0645 \u0647\u0633\u062a\u0647 \u0647\u0627\u06cc \u062f\u0633\u062a\u06af\u0627\u0647 \u0645\u0627 \u0628\u0627 \u062a\u0646\u0638\u06cc\u0645 \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc \u0634\u0648\u062f. n_jobs=-1<\/p>\n<p># Load libraries<br \/>\nimport numpy as np<br \/>\nfrom sklearn import linear_model, datasets<br \/>\nfrom sklearn.model_selection import GridSearchCV<\/p>\n<p># Load data<br \/>\niris = datasets.load_iris()<br \/>\nfeatures = iris.data<br \/>\ntarget = iris.target<\/p>\n<p># Create logistic regression<br \/>\nlogistic = linear_model.LogisticRegression(max_iter=500,<br \/>\n                                           solver=&#8221;liblinear&#8221;)<\/p>\n<p># Create range of candidate regularization penalty hyperparameter values<br \/>\npenalty = [&#8220;l1&#8221;, &#8220;l2&#8243;]\n<p># Create range of candidate values for C<br \/>\nC = np.logspace(0, 4, 1000)<\/p>\n<p># Create hyperparameter options<br \/>\nhyperparameters = dict(C=C, penalty=penalty)<\/p>\n<p># Create grid search<br \/>\ngridsearch = GridSearchCV(logistic, hyperparameters, cv=5, n_jobs=-1,<br \/>\n                             verbose=1)<\/p>\n<p># Fit grid search<br \/>\nbest_model = gridsearch.fit(features, target)<\/p>\n<p># Print best model<br \/>\nprint(best_model.best_estimator_)<\/p>\n<p># Fitting 5 folds for each of 2000 candidates, totalling 10000 fits<br \/>\n# LogisticRegression(C=5.926151812475554, max_iter=500, penalty=&#8217;l1&#8242;,<br \/>\n                                                  solver=&#8221;liblinear&#8221;)<\/p>\n<p>    \u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/p>\n<p>    \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/p>\n<p>  6. \u0627\u0641\u0632\u0627\u06cc\u0634 \u0633\u0631\u0639\u062a \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 (\u0631\u0648\u0634 \u0647\u0627\u06cc \u062e\u0627\u0635 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645).<\/p>\n<p>\u0627\u06cc\u0646 \u0631\u0627\u0647\u06cc \u0628\u0631\u0627\u06cc \u0627\u0641\u0632\u0627\u06cc\u0634 \u0633\u0631\u0639\u062a \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 \u0628\u062f\u0648\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0648\u0627\u0646 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0627\u0636\u0627\u0641\u06cc \u0627\u0633\u062a.<\/p>\n<p>\u0627\u06cc\u0646 \u0627\u0645\u06a9\u0627\u0646 \u067e\u0630\u06cc\u0631 \u0627\u0633\u062a \u0632\u06cc\u0631\u0627 scikit-learn \u062f\u0627\u0631\u0627\u06cc \u062a\u0646\u0638\u06cc\u0645 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u0627\u0639\u062a\u0628\u0627\u0631 \u0645\u062a\u0642\u0627\u0628\u0644 \u0645\u062f\u0644 \u062e\u0627\u0635 \u0627\u0633\u062a.<\/p>\n<p>\u06af\u0627\u0647\u06cc \u0627\u0648\u0642\u0627\u062a \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u06cc\u06a9 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0628\u0647 \u0645\u0627 \u0627\u0645\u06a9\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u062a\u0627 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0647\u0627\u06cc\u067e\u0631\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627 \u0631\u0627 \u0628\u0627 \u0633\u0631\u0639\u062a \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u0633\u0631\u06cc\u0639\u062a\u0631 \u062c\u0633\u062a\u062c\u0648 \u06a9\u0646\u06cc\u0645.<\/p>\n<p>\u0645\u062b\u0627\u0644:LogisticRegression \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u06cc\u06a9 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u06a9\u0646\u0646\u062f\u0647 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0644\u062c\u0633\u062a\u06cc\u06a9 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.LogisticRegressionCV \u06cc\u06a9 \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc\u200c\u06a9\u0646\u0646\u062f\u0647 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0644\u062c\u0633\u062a\u06cc\u06a9 \u0645\u0639\u062a\u0628\u0631 \u0645\u062a\u0642\u0627\u0637\u0639 \u06a9\u0627\u0631\u0622\u0645\u062f \u0631\u0627 \u067e\u06cc\u0627\u062f\u0647\u200c\u0633\u0627\u0632\u06cc \u0645\u06cc\u200c\u06a9\u0646\u062f \u06a9\u0647 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u0645\u0642\u062f\u0627\u0631 \u0628\u0647\u06cc\u0646\u0647 \u0627\u0628\u0631\u067e\u0627\u0631\u0627\u0645\u062a\u0631 C \u0631\u0627 \u0634\u0646\u0627\u0633\u0627\u06cc\u06cc \u06a9\u0646\u062f.<\/p>\n<p># Load libraries<br \/>\nfrom sklearn import linear_model, datasets<\/p>\n<p># Load data<br \/>\niris = datasets.load_iris()<br \/>\nfeatures = iris.data<br \/>\ntarget = iris.target<\/p>\n<p># Create cross-validated logistic regression<br \/>\nlogit = linear_model.LogisticRegressionCV(Cs=100, max_iter=500,<br \/>\n                                            solver=&#8221;liblinear&#8221;)<\/p>\n<p># Train model<br \/>\nlogit.fit(features, target)<\/p>\n<p># Print model<br \/>\nprint(logit)<\/p>\n<p># LogisticRegressionCV(Cs=100, max_iter=500, solver=&#8221;liblinear&#8221;)<\/p>\n<p>    \u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/p>\n<p>    \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/p>\n<p>\u062a\u0648\u062c\u0647:\u06cc\u06a9 \u0646\u0642\u0637\u0647 \u0636\u0639\u0641 \u0639\u0645\u062f\u0647 \u0628\u0631\u0627\u06cc LogisticRegressionCV \u0627\u06cc\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0641\u0642\u0637 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0645\u062d\u062f\u0648\u062f\u0647 \u0627\u06cc \u0627\u0632 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0631\u0627 \u0628\u0631\u0627\u06cc C \u062c\u0633\u062a\u062c\u0648 \u06a9\u0646\u062f. \u0627\u06cc\u0646 \u0645\u062d\u062f\u0648\u062f\u06cc\u062a \u062f\u0631 \u0628\u0633\u06cc\u0627\u0631\u06cc \u0627\u0632 \u0631\u0648\u06cc\u06a9\u0631\u062f\u0647\u0627\u06cc \u0627\u0639\u062a\u0628\u0627\u0631\u0633\u0646\u062c\u06cc \u0645\u062a\u0642\u0627\u0628\u0644 \u0645\u062f\u0644 \u062e\u0627\u0635 scikit-learn \u0645\u0634\u062a\u0631\u06a9 \u0627\u0633\u062a.<\/p>\n<p>\u0627\u0645\u06cc\u062f\u0648\u0627\u0631\u0645 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u0627\u06cc\u062c\u0627\u062f \u06cc\u06a9 \u0646\u0645\u0627\u06cc \u06a9\u0644\u06cc \u0633\u0631\u06cc\u0639 \u0627\u0632 \u0646\u062d\u0648\u0647 \u0627\u0646\u062a\u062e\u0627\u0628 \u06cc\u06a9 \u0645\u062f\u0644 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0645\u0641\u06cc\u062f \u0628\u0648\u062f\u0647 \u0628\u0627\u0634\u062f.<\/p>\n<div data-article-id=\"2013363\" id=\"article-body\">\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_85 counter-hierarchy ez-toc-counter-rtl ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">\u0641\u0647\u0631\u0633\u062a \u0645\u0637\u0627\u0644\u0628<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"\u062a\u063a\u06cc\u06cc\u0631 \u0648\u0636\u0639\u06cc\u062a \u0641\u0647\u0631\u0633\u062a \u0645\u0637\u0627\u0644\u0628\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/nabfollower.com\/blog\/ml-model-selection-1437\/#1_%D9%85%D9%82%D8%AF%D9%85%D9%87\" >1. \u0645\u0642\u062f\u0645\u0647<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/nabfollower.com\/blog\/ml-model-selection-1437\/#2_%D8%A7%D8%B3%D8%AA%D9%81%D8%A7%D8%AF%D9%87_%D8%A7%D8%B2_%D8%AC%D8%B3%D8%AA%D8%AC%D9%88%DB%8C_%D8%AC%D8%A7%D9%85%D8%B9\" >2. \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062c\u0633\u062a\u062c\u0648\u06cc \u062c\u0627\u0645\u0639.<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/nabfollower.com\/blog\/ml-model-selection-1437\/#3_%D8%A7%D8%B3%D8%AA%D9%81%D8%A7%D8%AF%D9%87_%D8%A7%D8%B2_%D8%AC%D8%B3%D8%AA%D8%AC%D9%88%DB%8C_%D8%AA%D8%B5%D8%A7%D8%AF%D9%81%DB%8C\" >3. \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062c\u0633\u062a\u062c\u0648\u06cc \u062a\u0635\u0627\u062f\u0641\u06cc.<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/nabfollower.com\/blog\/ml-model-selection-1437\/#4_%D8%A7%D9%86%D8%AA%D8%AE%D8%A7%D8%A8_%D8%A8%D9%87%D8%AA%D8%B1%DB%8C%D9%86_%D9%85%D8%AF%D9%84_%D9%87%D8%A7_%D8%A7%D8%B2_%D8%A7%D9%84%DA%AF%D9%88%D8%B1%DB%8C%D8%AA%D9%85_%D9%87%D8%A7%DB%8C_%DB%8C%D8%A7%D8%AF%DA%AF%DB%8C%D8%B1%DB%8C_%DA%86%D9%86%D8%AF%DA%AF%D8%A7%D9%86%D9%87\" >4. \u0627\u0646\u062a\u062e\u0627\u0628 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0647\u0627 \u0627\u0632 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0686\u0646\u062f\u06af\u0627\u0646\u0647.<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/nabfollower.com\/blog\/ml-model-selection-1437\/#5_%D8%A7%D9%86%D8%AA%D8%AE%D8%A7%D8%A8_%D8%A8%D9%87%D8%AA%D8%B1%DB%8C%D9%86_%D9%85%D8%AF%D9%84_%D9%87%D9%86%DA%AF%D8%A7%D9%85_%D9%BE%DB%8C%D8%B4_%D9%BE%D8%B1%D8%AF%D8%A7%D8%B2%D8%B4\" >5. \u0627\u0646\u062a\u062e\u0627\u0628 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0647\u0646\u06af\u0627\u0645 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634.<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/nabfollower.com\/blog\/ml-model-selection-1437\/#5_%D8%A7%D9%81%D8%B2%D8%A7%DB%8C%D8%B4_%D8%B3%D8%B1%D8%B9%D8%AA_%D8%A7%D9%86%D8%AA%D8%AE%D8%A7%D8%A8_%D9%85%D8%AF%D9%84_%D8%A8%D8%A7_%D9%85%D9%88%D8%A7%D8%B2%DB%8C_%D8%B3%D8%A7%D8%B2%DB%8C\" >5. \u0627\u0641\u0632\u0627\u06cc\u0634 \u0633\u0631\u0639\u062a \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 \u0628\u0627 \u0645\u0648\u0627\u0632\u06cc \u0633\u0627\u0632\u06cc.<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/nabfollower.com\/blog\/ml-model-selection-1437\/#6_%D8%A7%D9%81%D8%B2%D8%A7%DB%8C%D8%B4_%D8%B3%D8%B1%D8%B9%D8%AA_%D8%A7%D9%86%D8%AA%D8%AE%D8%A7%D8%A8_%D9%85%D8%AF%D9%84_%D8%B1%D9%88%D8%B4_%D9%87%D8%A7%DB%8C_%D8%AE%D8%A7%D8%B5_%D8%A7%D9%84%DA%AF%D9%88%D8%B1%DB%8C%D8%AA%D9%85\" >6. \u0627\u0641\u0632\u0627\u06cc\u0634 \u0633\u0631\u0639\u062a \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 (\u0631\u0648\u0634 \u0647\u0627\u06cc \u062e\u0627\u0635 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645).<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"1_%D9%85%D9%82%D8%AF%D9%85%D9%87\"><\/span>\n<p>  1. \u0645\u0642\u062f\u0645\u0647<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u06cc\u0627\u062f \u0645\u06cc\u200c\u06af\u06cc\u0631\u06cc\u0645 \u06a9\u0647 \u0686\u06af\u0648\u0646\u0647 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0631\u0627 \u0628\u06cc\u0646 \u0686\u0646\u062f\u06cc\u0646 \u0645\u062f\u0644 \u0628\u0627 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u0646\u06cc\u0645\u060c \u062f\u0631 \u0628\u0631\u062e\u06cc \u0645\u0648\u0627\u0631\u062f \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0628\u06cc\u0634 \u0627\u0632 50 \u0645\u062f\u0644 \u0645\u062e\u062a\u0644\u0641 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u0645\u060c \u062f\u0627\u0646\u0633\u062a\u0646 \u0646\u062d\u0648\u0647 \u0627\u0646\u062a\u062e\u0627\u0628 \u06cc\u06a9\u06cc \u0628\u0631\u0627\u06cc \u0628\u0647 \u062f\u0633\u062a \u0622\u0648\u0631\u062f\u0646 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0639\u0645\u0644\u06a9\u0631\u062f \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0634\u0645\u0627 \u0645\u0647\u0645 \u0627\u0633\u062a.<\/p>\n<p>\u0645\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 \u0631\u0627 \u0647\u0645 \u0628\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0648 \u0647\u0645 \u0628\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0647\u0627\u06cc\u067e\u0631\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0622\u0646 \u0627\u0646\u062c\u0627\u0645 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0627\u062f.<\/p>\n<p>\u0627\u0645\u0627 \u0627\u0648\u0644 \u0686\u0647 \u0647\u0633\u062a\u0646\u062f <strong>\u0647\u0627\u06cc\u067e\u0631\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627<\/strong>? \u0627\u06cc\u0646\u0647\u0627 \u062a\u0646\u0638\u06cc\u0645\u0627\u062a \u0627\u0636\u0627\u0641\u06cc \u0647\u0633\u062a\u0646\u062f \u06a9\u0647 \u062a\u0648\u0633\u0637 \u06a9\u0627\u0631\u0628\u0631 \u062a\u0646\u0638\u06cc\u0645 \u0645\u06cc \u0634\u0648\u0646\u062f \u0648 \u0628\u0631 \u0646\u062d\u0648\u0647 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u062f\u0644 \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0622\u0646 \u062a\u0623\u062b\u06cc\u0631 \u0645\u06cc \u06af\u0630\u0627\u0631\u0646\u062f. <strong>\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627<\/strong> \u0627\u0632 \u0633\u0648\u06cc \u062f\u06cc\u06af\u0631\u060c \u0645\u062f\u0644 \u0647\u0627 \u062f\u0631 \u0637\u0648\u0644 \u0641\u0631\u0622\u06cc\u0646\u062f \u0622\u0645\u0648\u0632\u0634 \u06cc\u0627\u062f \u0645\u06cc \u06af\u06cc\u0631\u0646\u062f.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"2_%D8%A7%D8%B3%D8%AA%D9%81%D8%A7%D8%AF%D9%87_%D8%A7%D8%B2_%D8%AC%D8%B3%D8%AA%D8%AC%D9%88%DB%8C_%D8%AC%D8%A7%D9%85%D8%B9\"><\/span>\n<p>  2. \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062c\u0633\u062a\u062c\u0648\u06cc \u062c\u0627\u0645\u0639.<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>\u062c\u0633\u062a\u062c\u0648\u06cc \u062c\u0627\u0645\u0639<\/strong> \u0634\u0627\u0645\u0644 \u0627\u0646\u062a\u062e\u0627\u0628 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0628\u0627 \u062c\u0633\u062a\u062c\u0648 \u062f\u0631 \u0637\u06cc\u0641 \u0648\u0633\u06cc\u0639\u06cc \u0627\u0632 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627 \u0627\u0633\u062a. \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0645\u0627 \u0627\u0632 scikit-learn \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645 <strong><em>GridSearchCV<\/em><\/strong>.<\/p>\n<p><strong><em>GridSearchCV \u0686\u06af\u0648\u0646\u0647 \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u062f:<\/em><\/strong><\/p>\n<ol>\n<li>\u06a9\u0627\u0631\u0628\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u0627\u06cc \u0627\u0632 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0645\u0645\u06a9\u0646 \u0631\u0627 \u0628\u0631\u0627\u06cc \u06cc\u06a9 \u06cc\u0627 \u0686\u0646\u062f \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u062a\u0639\u0631\u06cc\u0641 \u0645\u06cc \u06a9\u0646\u062f.<\/li>\n<li>GridSearchCV \u06cc\u06a9 \u0645\u062f\u0644 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0647\u0631 \u0645\u0642\u062f\u0627\u0631 \u0648 \/ \u06cc\u0627 \u062a\u0631\u06a9\u06cc\u0628\u06cc \u0627\u0632 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0622\u0645\u0648\u0632\u0634 \u0645\u06cc \u062f\u0647\u062f.<\/li>\n<li>\u0645\u062f\u0644 \u0628\u0627 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0639\u0645\u0644\u06a9\u0631\u062f \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u06cc \u0634\u0648\u062f.<\/li>\n<\/ol>\n<p><strong><em>\u0645\u062b\u0627\u0644<\/em><\/strong><br \/>\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u06cc\u06a9 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0644\u062c\u0633\u062a\u06cc\u06a9 \u0631\u0627 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062e\u0648\u062f \u062a\u0646\u0638\u06cc\u0645 \u06a9\u0646\u06cc\u0645 \u0648 \u062f\u0648 \u0627\u0628\u0631\u067e\u0627\u0631\u0627\u0645\u062a\u0631 (C \u0648 \u062c\u0631\u06cc\u0645\u0647 \u0645\u0646\u0638\u0645 \u0633\u0627\u0632\u06cc) \u0631\u0627 \u062a\u0646\u0638\u06cc\u0645 \u06a9\u0646\u06cc\u0645. \u0647\u0645\u0686\u0646\u06cc\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u062f\u0648 \u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u062a\u06a9\u0631\u0627\u0631 \u062d\u0644 \u06a9\u0646\u0646\u062f\u0647 \u0648 \u062d\u062f\u0627\u06a9\u062b\u0631 \u0631\u0627 \u0645\u0634\u062e\u0635 \u06a9\u0646\u06cc\u0645.<\/p>\n<p>\u0627\u06a9\u0646\u0648\u0646 \u0628\u0631\u0627\u06cc \u0647\u0631 \u062a\u0631\u06a9\u06cc\u0628\u06cc \u0627\u0632 \u0645\u0642\u0627\u062f\u06cc\u0631 \u062c\u0631\u06cc\u0645\u0647 C \u0648 \u0645\u0646\u0638\u0645 \u0633\u0627\u0632\u06cc\u060c \u0645\u062f\u0644 \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u0645\u06cc \u062f\u0647\u06cc\u0645 \u0648 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0627\u0639\u062a\u0628\u0627\u0631\u0633\u0646\u062c\u06cc \u0645\u062a\u0642\u0627\u0637\u0639 k-fold \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u0645.<br \/>\u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0645\u0627 10 \u0645\u0642\u062f\u0627\u0631 \u0645\u0645\u06a9\u0646 \u0628\u0631\u0627\u06cc C \u062f\u0627\u0631\u06cc\u0645\u060c 2 \u0645\u0642\u062f\u0627\u0631 \u0645\u0645\u06a9\u0646 \u0628\u0631\u0627\u06cc reg. \u067e\u0646\u0627\u0644\u062a\u06cc \u0648 5 \u0628\u0631\u0627\u0628\u0631 \u0645\u0627 \u062f\u0631 \u0645\u062c\u0645\u0648\u0639 (10*2*5 = 100) \u0645\u062f\u0644\u0647\u0627\u06cc \u06a9\u0627\u0646\u062f\u06cc\u062f \u062f\u0627\u0631\u06cc\u0645 \u06a9\u0647 \u0627\u0632 \u0628\u06cc\u0646 \u0622\u0646\u0647\u0627 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight plaintext\"><code># Load libraries\nimport numpy as np\nfrom sklearn import linear_model, datasets\nfrom sklearn.model_selection import GridSearchCV\n\n# Load data\niris = datasets.load_iris()\nfeatures = iris.data\ntarget = iris.target\n\n# Create logistic regression\nlogistic = linear_model.LogisticRegression(max_iter=500, solver=\"liblinear\")\n\n# Create range of candidate penalty hyperparameter values\npenalty = ['l1','l2']\n\n# Create range of candidate regularization hyperparameter values\nC = np.logspace(0, 4, 10)\n\n# Create dictionary of hyperparameter candidates\nhyperparameters = dict(C=C, penalty=penalty)\n\n# Create grid search\ngridsearch = GridSearchCV(logistic, hyperparameters, cv=5, verbose=0)\n\n# Fit grid search\nbest_model = gridsearch.fit(features, target)\n\n# Show the best model\nprint(best_model.best_estimator_)\n\n# LogisticRegression(C=7.742636826811269, max_iter=500, penalty='l1',\nsolver=\"liblinear\") # Result\n\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p><strong>\u06af\u0631\u0641\u062a\u0646 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644<\/strong>:<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight plaintext\"><code># View best hyperparameters\nprint('Best Penalty:', best_model.best_estimator_.get_params()['penalty'])\nprint('Best C:', best_model.best_estimator_.get_params()['C'])\n\n# Best Penalty: l1 #Result\n# Best C: 7.742636826811269 # Result\n\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"3_%D8%A7%D8%B3%D8%AA%D9%81%D8%A7%D8%AF%D9%87_%D8%A7%D8%B2_%D8%AC%D8%B3%D8%AA%D8%AC%D9%88%DB%8C_%D8%AA%D8%B5%D8%A7%D8%AF%D9%81%DB%8C\"><\/span>\n<p>  3. \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062c\u0633\u062a\u062c\u0648\u06cc \u062a\u0635\u0627\u062f\u0641\u06cc.<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u06cc\u0646 \u0645\u0639\u0645\u0648\u0644\u0627\u064b \u0632\u0645\u0627\u0646\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f \u06a9\u0647 \u0628\u0631\u0627\u06cc \u0627\u0646\u062a\u062e\u0627\u0628 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644\u060c \u0631\u0648\u0634 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0627\u0631\u0632\u0627\u0646\u200c\u062a\u0631\u06cc \u0646\u0633\u0628\u062a \u0628\u0647 \u062c\u0633\u062a\u062c\u0648\u06cc \u062c\u0627\u0645\u0639 \u0645\u06cc\u200c\u062e\u0648\u0627\u0647\u06cc\u062f.<\/p>\n<p>\u0634\u0627\u06cc\u0627\u0646 \u0630\u06a9\u0631 \u0627\u0633\u062a \u06a9\u0647 \u062f\u0644\u06cc\u0644 \u0627\u06cc\u0646\u06a9\u0647 RandomizedSearchCV \u0630\u0627\u062a\u0627\u064b \u0633\u0631\u06cc\u0639\u062a\u0631 \u0627\u0632 GridSearchCV \u0646\u06cc\u0633\u062a\u060c \u0627\u0645\u0627 \u0627\u063a\u0644\u0628 \u0628\u0627 \u0622\u0632\u0645\u0627\u06cc\u0634 \u062a\u0631\u06a9\u06cc\u0628\u0627\u062a \u06a9\u0645\u062a\u0631\u060c \u0639\u0645\u0644\u06a9\u0631\u062f\u06cc \u0642\u0627\u0628\u0644 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0628\u0627 GridSearchCV \u062f\u0631 \u0632\u0645\u0627\u0646 \u06a9\u0645\u062a\u0631\u06cc \u062f\u0627\u0631\u062f.<\/p>\n<p><strong><em>\u0646\u062d\u0648\u0647 \u06a9\u0627\u0631\u06a9\u0631\u062f \u062a\u0635\u0627\u062f\u0641\u06cc \u062c\u0633\u062a\u062c\u0648\u06cc CV<\/em><\/strong>:<\/p>\n<ol>\n<li>\u06a9\u0627\u0631\u0628\u0631 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\/\u062a\u0648\u0632\u06cc\u0639\u0627\u062a (\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644 \u0646\u0631\u0645\u0627\u0644\u060c \u06cc\u06a9\u0646\u0648\u0627\u062e\u062a) \u0631\u0627 \u0639\u0631\u0636\u0647 \u0645\u06cc \u06a9\u0646\u062f.<\/li>\n<li>\u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645\u200c\u0647\u0627 \u0628\u0647\u200c\u0637\u0648\u0631 \u062a\u0635\u0627\u062f\u0641\u06cc \u062a\u0639\u062f\u0627\u062f \u062e\u0627\u0635\u06cc \u0627\u0632 \u062a\u0631\u06a9\u06cc\u0628\u200c\u0647\u0627\u06cc \u062a\u0635\u0627\u062f\u0641\u06cc \u0645\u0642\u0627\u062f\u06cc\u0631 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u062f\u0627\u062f\u0647\u200c\u0634\u062f\u0647 \u0631\u0627 \u0628\u062f\u0648\u0646 \u062c\u0627\u06cc\u06af\u0632\u06cc\u0646\u06cc \u062c\u0633\u062a\u062c\u0648 \u0645\u06cc\u200c\u06a9\u0646\u0646\u062f.<\/li>\n<\/ol>\n<p><strong>\u0645\u062b\u0627\u0644<\/strong><\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight plaintext\"><code># Load data\niris = datasets.load_iris()\nfeatures = iris.data\ntarget = iris.target\n\n# Create logistic regression\nlogistic = linear_model.LogisticRegression(max_iter=500, solver=\"liblinear\")\n\n# Create range of candidate regularization penalty hyperparameter values\npenalty = ['l1', 'l2']\n\n# Create distribution of candidate regularization hyperparameter values\nC = uniform(loc=0, scale=4)\n\n# Create hyperparameter options\nhyperparameters = dict(C=C, penalty=penalty)\n\n# Create randomized search\nrandomizedsearch = RandomizedSearchCV(\nlogistic, hyperparameters, random_state=1, n_iter=100, cv=5, verbose=0,\nn_jobs=-1)\n\n# Fit randomized search\nbest_model = randomizedsearch.fit(features, target)\n\n# Print best model\nprint(best_model.best_estimator_)\n\n# LogisticRegression(C=1.668088018810296, max_iter=500, penalty='l1',\nsolver=\"liblinear\") #Result.\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p><strong>\u062f\u0631\u06cc\u0627\u0641\u062a \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644:<\/strong><\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight plaintext\"><code># View best hyperparameters\nprint('Best Penalty:', best_model.best_estimator_.get_params()['penalty'])\nprint('Best C:', best_model.best_estimator_.get_params()['C'])\n\n# Best Penalty: l1 # Result\n# Best C: 1.668088018810296 # Result\n\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p><strong>\u062a\u0648\u062c\u0647:<\/strong> \u062a\u0639\u062f\u0627\u062f \u0645\u062f\u0644 \u0647\u0627\u06cc \u06a9\u0627\u0646\u062f\u06cc\u062f \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647 \u062f\u0631 \u0645\u0634\u062e\u0635 \u0634\u062f\u0647 \u0627\u0633\u062a <strong>n_iter<\/strong> \u062a\u0646\u0638\u06cc\u0645\u0627\u062a (\u062a\u0639\u062f\u0627\u062f \u062a\u06a9\u0631\u0627\u0631).<\/p>\n<h2><span class=\"ez-toc-section\" id=\"4_%D8%A7%D9%86%D8%AA%D8%AE%D8%A7%D8%A8_%D8%A8%D9%87%D8%AA%D8%B1%DB%8C%D9%86_%D9%85%D8%AF%D9%84_%D9%87%D8%A7_%D8%A7%D8%B2_%D8%A7%D9%84%DA%AF%D9%88%D8%B1%DB%8C%D8%AA%D9%85_%D9%87%D8%A7%DB%8C_%DB%8C%D8%A7%D8%AF%DA%AF%DB%8C%D8%B1%DB%8C_%DA%86%D9%86%D8%AF%DA%AF%D8%A7%D9%86%D9%87\"><\/span>\n<p>  4. \u0627\u0646\u062a\u062e\u0627\u0628 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0647\u0627 \u0627\u0632 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0686\u0646\u062f\u06af\u0627\u0646\u0647.<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0628\u062e\u0634 \u0628\u0647 \u0646\u062d\u0648\u0647 \u0627\u0646\u062a\u062e\u0627\u0628 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0628\u0627 \u062c\u0633\u062a\u062c\u0648 \u062f\u0631 \u0637\u06cc\u0641 \u0648\u0633\u06cc\u0639\u06cc \u0627\u0632 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645\u200c\u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0648 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0645\u0631\u0628\u0648\u0637\u0647 \u0645\u06cc\u200c\u067e\u0631\u062f\u0627\u0632\u06cc\u0645.<\/p>\n<p>\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0631\u0627 \u0628\u0647 \u0633\u0627\u062f\u06af\u06cc \u0628\u0627 \u0627\u06cc\u062c\u0627\u062f \u0641\u0631\u0647\u0646\u06af \u0644\u063a\u062a \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0646\u0627\u0645\u0632\u062f \u0648 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0622\u0646\u0647\u0627 \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u0645 \u062a\u0627 \u0627\u0632 \u0622\u0646\u0647\u0627 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0641\u0636\u0627\u06cc \u062c\u0633\u062a\u062c\u0648 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645. <strong><em>GridSearchCV<\/em><\/strong>.<\/p>\n<p><strong>\u0645\u0631\u0627\u062d\u0644:<\/strong><\/p>\n<ol>\n<li>\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0641\u0636\u0627\u06cc \u062c\u0633\u062a\u062c\u0648\u06cc\u06cc \u0631\u0627 \u062a\u0639\u0631\u06cc\u0641 \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u0634\u0627\u0645\u0644 \u062f\u0648 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0627\u0633\u062a.<\/li>\n<li>\u0645\u0627 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627 \u0631\u0627 \u0645\u0634\u062e\u0635 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u0648 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0642\u0627\u0644\u0628\u060c \u0645\u0642\u0627\u062f\u06cc\u0631 \u06a9\u0627\u0646\u062f\u06cc\u062f \u0622\u0646\u0647\u0627 \u0631\u0627 \u062a\u0639\u0631\u06cc\u0641 \u0645\u06cc \u06a9\u0646\u06cc\u0645 <strong><em>\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u06a9\u0646\u0646\u062f\u0647<\/em>[hyperparameter name]_<\/strong>.\n<\/li>\n<\/ol>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight plaintext\"><code># Load libraries\nimport numpy as np\nfrom sklearn import datasets\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.pipeline import Pipeline\n\n# Set random seed\nnp.random.seed(0)\n\n# Load data\niris = datasets.load_iris()\nfeatures = iris.data\ntarget = iris.target\n\n# Create a pipeline\npipe = Pipeline([(\"classifier\", RandomForestClassifier())])\n\n# Create dictionary with candidate learning algorithms and their hyperparameters\nsearch_space = [{\"classifier\": [LogisticRegression(max_iter=500,\nsolver=\"liblinear\")],\n\"classifier__penalty\": ['l1', 'l2'],\n\"classifier__C\": np.logspace(0, 4, 10)},\n{\"classifier\": [RandomForestClassifier()],\n\"classifier__n_estimators\": [10, 100, 1000],\n\"classifier__max_features\": [1, 2, 3]}]\n\n# Create grid search\ngridsearch = GridSearchCV(pipe, search_space, cv=5, verbose=0)\n\n# Fit grid search\nbest_model = gridsearch.fit(features, target)\n\n# Print best model\nprint(best_model.best_estimator_)\n\n# Pipeline(steps=[('classifier',\n                 LogisticRegression(C=7.742636826811269, max_iter=500,\n                      penalty='l1', solver=\"liblinear\"))])\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p><strong>\u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644:<\/strong><br \/>\u067e\u0633 \u0627\u0632 \u0627\u062a\u0645\u0627\u0645 \u062c\u0633\u062a\u062c\u0648 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645 <strong>best_estimator_<\/strong> \u0628\u0631\u0627\u06cc \u0645\u0634\u0627\u0647\u062f\u0647 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0648 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0645\u062f\u0644.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"5_%D8%A7%D9%86%D8%AA%D8%AE%D8%A7%D8%A8_%D8%A8%D9%87%D8%AA%D8%B1%DB%8C%D9%86_%D9%85%D8%AF%D9%84_%D9%87%D9%86%DA%AF%D8%A7%D9%85_%D9%BE%DB%8C%D8%B4_%D9%BE%D8%B1%D8%AF%D8%A7%D8%B2%D8%B4\"><\/span>\n<p>  5. \u0627\u0646\u062a\u062e\u0627\u0628 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0647\u0646\u06af\u0627\u0645 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634.<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u06af\u0627\u0647\u06cc \u0627\u0648\u0642\u0627\u062a \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0628\u062e\u0648\u0627\u0647\u06cc\u0645 \u06cc\u06a9 \u0645\u0631\u062d\u0644\u0647 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0631\u0627 \u062f\u0631 \u0637\u0648\u0644 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 \u0644\u062d\u0627\u0638 \u06a9\u0646\u06cc\u0645.<br \/>\u0628\u0647\u062a\u0631\u06cc\u0646 \u0631\u0627\u0647 \u062d\u0644 \u0627\u06cc\u062c\u0627\u062f \u062e\u0637 \u0644\u0648\u0644\u0647 \u0627\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0634\u0627\u0645\u0644 \u0645\u0631\u062d\u0644\u0647 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0648 \u0647\u0631 \u06cc\u06a9 \u0627\u0632 \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0622\u0646 \u0628\u0627\u0634\u062f:<\/p>\n<p><strong>\u0686\u0627\u0644\u0634 \u0627\u0648\u0644<\/strong>:<br \/>GridSeachCv \u0627\u0632 \u0627\u0639\u062a\u0628\u0627\u0631\u0633\u0646\u062c\u06cc \u0645\u062a\u0642\u0627\u0628\u0644 \u0628\u0631\u0627\u06cc \u062a\u0639\u06cc\u06cc\u0646 \u0645\u062f\u0644 \u0628\u0627 \u0628\u0627\u0644\u0627\u062a\u0631\u06cc\u0646 \u0639\u0645\u0644\u06a9\u0631\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<p>\u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u062f\u0631 \u0627\u0639\u062a\u0628\u0627\u0631\u0633\u0646\u062c\u06cc \u0645\u062a\u0642\u0627\u0637\u0639\u060c \u0645\u0627 \u0648\u0627\u0646\u0645\u0648\u062f \u0645\u06cc\u200c\u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u0686\u06cc\u0646 \u0628\u0647\u200c\u0639\u0646\u0648\u0627\u0646 \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u062f\u06cc\u062f\u0647 \u0646\u0645\u06cc\u200c\u0634\u0648\u062f\u060c \u0648 \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0628\u062e\u0634\u06cc \u0627\u0632 \u0628\u0631\u0627\u0632\u0634 \u0647\u0631 \u0645\u0631\u062d\u0644\u0647 \u067e\u06cc\u0634\u200c\u067e\u0631\u062f\u0627\u0632\u0634 (\u0645\u0627\u0646\u0646\u062f \u0645\u0642\u06cc\u0627\u0633\u200c\u0628\u0646\u062f\u06cc \u06cc\u0627 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f\u0633\u0627\u0632\u06cc) \u0646\u06cc\u0633\u062a.<\/p>\n<p>\u0628\u0647 \u0647\u0645\u06cc\u0646 \u062f\u0644\u06cc\u0644 \u0645\u0631\u0627\u062d\u0644 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0628\u0627\u06cc\u062f \u0628\u062e\u0634\u06cc \u0627\u0632 \u0645\u062c\u0645\u0648\u0639\u0647 \u0627\u0642\u062f\u0627\u0645\u0627\u062a \u0627\u0646\u062c\u0627\u0645 \u0634\u062f\u0647 \u062a\u0648\u0633\u0637 GridSearchCV \u0628\u0627\u0634\u062f.<\/p>\n<p><strong>\u0631\u0627\u0647 \u062d\u0644<\/strong><br \/>Scikit-learn \u0641\u0631\u0627\u0647\u0645 \u0645\u06cc \u06a9\u0646\u062f <strong>FeatureUnion<\/strong> \u06a9\u0647 \u0628\u0647 \u0645\u0627 \u0627\u0645\u06a9\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u0686\u0646\u062f\u06cc\u0646 \u0639\u0645\u0644\u06cc\u0627\u062a \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0631\u0627 \u0628\u0647 \u062f\u0631\u0633\u062a\u06cc \u062a\u0631\u06a9\u06cc\u0628 \u06a9\u0646\u06cc\u0645.<br \/><strong>\u0645\u0631\u0627\u062d\u0644:<\/strong><\/p>\n<ol>\n<li>\u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645 <strong>_ FeatureUnion _<\/strong>\u0628\u0631\u0627\u06cc \u062a\u0631\u06a9\u06cc\u0628 \u062f\u0648 \u0645\u0631\u062d\u0644\u0647 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634: \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u06a9\u0631\u062f\u0646 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0648\u06cc\u0698\u06af\u06cc (<strong><em>StandardScaler<\/em><\/strong>) \u0648 \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0645\u0624\u0644\u0641\u0647 \u0647\u0627\u06cc \u0627\u0635\u0644\u06cc (<strong><em>PCA<\/em><\/strong>) &#8211; \u0627\u06cc\u0646 \u0634\u06cc \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0646\u0627\u0645\u06cc\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f \u0648 \u0634\u0627\u0645\u0644 \u0647\u0631 \u062f\u0648 \u0645\u0631\u062d\u0644\u0647 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0645\u0627 \u0627\u0633\u062a.<\/li>\n<li>\u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f\u060c \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0631\u0627 \u0628\u0627 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062e\u0648\u062f \u062f\u0631 \u062e\u0637 \u0644\u0648\u0644\u0647 \u062e\u0648\u062f \u0642\u0631\u0627\u0631 \u0645\u06cc \u062f\u0647\u06cc\u0645.<\/li>\n<\/ol>\n<p>\u0627\u06cc\u0646 \u0628\u0647 \u0645\u0627 \u0627\u0645\u06a9\u0627\u0646 \u0645\u06cc\u200c\u062f\u0647\u062f \u062a\u0627 \u0645\u062f\u06cc\u0631\u06cc\u062a \u0645\u0646\u0627\u0633\u0628 \u0628\u0631\u0627\u0632\u0634\u060c \u062a\u0628\u062f\u06cc\u0644 \u0648 \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644\u200c\u0647\u0627 \u0631\u0627 \u0628\u0627 \u062a\u0631\u06a9\u06cc\u0628\u06cc \u0627\u0632 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627 \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc scikit \u0628\u0631\u0648\u0646 \u0633\u067e\u0627\u0631\u06cc \u06a9\u0646\u06cc\u0645.<\/p>\n<p><strong>\u0686\u0627\u0644\u0634 \u062f\u0648\u0645:<\/strong><br \/>\u0628\u0631\u062e\u06cc \u0627\u0632 \u0631\u0648\u0634\u200c\u0647\u0627\u06cc \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0645\u0627\u0646\u0646\u062f PCA \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u062e\u0627\u0635 \u062e\u0648\u062f \u0631\u0627 \u062f\u0627\u0631\u0646\u062f\u060c \u06a9\u0627\u0647\u0634 \u0627\u0628\u0639\u0627\u062f \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 PCA \u0628\u0647 \u06a9\u0627\u0631\u0628\u0631 \u0646\u06cc\u0627\u0632 \u062f\u0627\u0631\u062f \u06a9\u0647 \u062a\u0639\u062f\u0627\u062f \u0627\u062c\u0632\u0627\u06cc \u0627\u0635\u0644\u06cc \u0631\u0627 \u0628\u0631\u0627\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0628\u0631\u0627\u06cc \u062a\u0648\u0644\u06cc\u062f \u0645\u062c\u0645\u0648\u0639\u0647 \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627\u06cc \u062a\u0628\u062f\u06cc\u0644\u200c\u0634\u062f\u0647 \u062a\u0639\u0631\u06cc\u0641 \u06a9\u0646\u062f. \u062f\u0631 \u062d\u0627\u0644\u062a \u0627\u06cc\u062f\u0647\u200c\u0622\u0644\u060c \u062a\u0639\u062f\u0627\u062f \u0645\u0624\u0644\u0641\u0647\u200c\u0647\u0627\u06cc\u06cc \u0631\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u06cc\u200c\u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u0645\u062f\u0644\u06cc \u0628\u0627 \u0628\u06cc\u0634\u062a\u0631\u06cc\u0646 \u0639\u0645\u0644\u06a9\u0631\u062f \u0631\u0627 \u0628\u0631\u0627\u06cc \u0628\u0631\u062e\u06cc \u0627\u0632 \u0645\u0639\u06cc\u0627\u0631\u0647\u0627\u06cc \u0622\u0632\u0645\u0648\u0646 \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u062a\u0648\u0644\u06cc\u062f \u0645\u06cc\u200c\u06a9\u0646\u0646\u062f.<br \/><strong>\u0631\u0627\u0647 \u062d\u0644.<\/strong><br \/>\u062f\u0631 scikit-learn \u0648\u0642\u062a\u06cc \u0645\u0642\u0627\u062f\u06cc\u0631 \u0645\u0624\u0644\u0641\u0647\u200c\u0647\u0627\u06cc \u0646\u0627\u0645\u0632\u062f \u0631\u0627 \u062f\u0631 \u0641\u0636\u0627\u06cc \u062c\u0633\u062a\u062c\u0648 \u0642\u0631\u0627\u0631 \u0645\u06cc\u200c\u062f\u0647\u06cc\u0645\u060c \u0645\u0627\u0646\u0646\u062f \u0647\u0631 \u0627\u0628\u0631\u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u062f\u06cc\u06af\u0631\u06cc \u06a9\u0647 \u0628\u0627\u06cc\u062f \u062c\u0633\u062a\u062c\u0648 \u0634\u0648\u062f\u060c \u0631\u0641\u062a\u0627\u0631 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight plaintext\"><code># Load libraries\nimport numpy as np\nfrom sklearn import datasets\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.pipeline import Pipeline, FeatureUnion\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import StandardScaler\n\n# Set random seed\nnp.random.seed(0)\n\n# Load data\niris = datasets.load_iris()\nfeatures = iris.data\ntarget = iris.target\n\n# Create a preprocessing object that includes StandardScaler features and PCA\npreprocess = FeatureUnion([(\"std\", StandardScaler()), (\"pca\", PCA())])\n\n# Create a pipeline\npipe = Pipeline([(\"preprocess\", preprocess),\n               (\"classifier\", LogisticRegression(max_iter=1000,\n               solver=\"liblinear\"))])\n\n# Create space of candidate values\nsearch_space = [{\"preprocess__pca__n_components\": [1, 2, 3],\n\"classifier__penalty\": [\"l1\", \"l2\"],\n\"classifier__C\": np.logspace(0, 4, 10)}]\n\n# Create grid search\nclf = GridSearchCV(pipe, search_space, cv=5, verbose=0, n_jobs=-1)\n\n# Fit grid search\nbest_model = clf.fit(features, target)\n\n# Print best model\nprint(best_model.best_estimator_)\n\n# Pipeline(steps=[('preprocess',\n     FeatureUnion(transformer_list=[('std', StandardScaler()),\n                                    ('pca', PCA(n_components=1))])),\n    ('classifier',\n    LogisticRegression(C=7.742636826811269, max_iter=1000,\n                      penalty='l1', solver=\"liblinear\"))]) # Result\n\n\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p>\u0628\u0639\u062f \u0627\u0632 \u0627\u06cc\u0646\u06a9\u0647 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 \u06a9\u0627\u0645\u0644 \u0634\u062f\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0645\u0642\u0627\u062f\u06cc\u0631 \u067e\u06cc\u0634\u200c\u067e\u0631\u062f\u0627\u0632\u0634 \u0631\u0627 \u06a9\u0647 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0631\u0627 \u062a\u0648\u0644\u06cc\u062f \u06a9\u0631\u062f\u0647\u200c\u0627\u0646\u062f\u060c \u0645\u0634\u0627\u0647\u062f\u0647 \u06a9\u0646\u06cc\u0645.<\/p>\n<p><strong>\u0645\u0631\u0627\u062d\u0644 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u06a9\u0647 \u0628\u0647\u062a\u0631\u06cc\u0646 \u062d\u0627\u0644\u062a \u0647\u0627 \u0631\u0627 \u062a\u0648\u0644\u06cc\u062f \u06a9\u0631\u062f<\/strong><\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight plaintext\"><code># View best n_components\n\nbest_model.best_estimator_.get_params() \n# ['preprocess__pca__n_components'] # Results\n\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"5_%D8%A7%D9%81%D8%B2%D8%A7%DB%8C%D8%B4_%D8%B3%D8%B1%D8%B9%D8%AA_%D8%A7%D9%86%D8%AA%D8%AE%D8%A7%D8%A8_%D9%85%D8%AF%D9%84_%D8%A8%D8%A7_%D9%85%D9%88%D8%A7%D8%B2%DB%8C_%D8%B3%D8%A7%D8%B2%DB%8C\"><\/span>\n<p>  5. \u0627\u0641\u0632\u0627\u06cc\u0634 \u0633\u0631\u0639\u062a \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 \u0628\u0627 \u0645\u0648\u0627\u0632\u06cc \u0633\u0627\u0632\u06cc.<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u062f\u0631 \u0622\u0646 \u0632\u0645\u0627\u0646 \u0628\u0627\u06cc\u062f \u0632\u0645\u0627\u0646 \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 \u0631\u0627 \u06a9\u0627\u0647\u0634 \u062f\u0647\u06cc\u062f.<br \/>\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0631\u0627 \u0628\u0627 \u0622\u0645\u0648\u0632\u0634 \u0686\u0646\u062f\u06cc\u0646 \u0645\u062f\u0644 \u0628\u0647 \u0637\u0648\u0631 \u0647\u0645\u0632\u0645\u0627\u0646 \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u0645\u060c \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0645\u0627\u0645 \u0647\u0633\u062a\u0647 \u0647\u0627\u06cc \u062f\u0633\u062a\u06af\u0627\u0647 \u0645\u0627 \u0628\u0627 \u062a\u0646\u0638\u06cc\u0645 \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc \u0634\u0648\u062f. <strong>n_jobs=-1<\/strong><\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight plaintext\"><code># Load libraries\nimport numpy as np\nfrom sklearn import linear_model, datasets\nfrom sklearn.model_selection import GridSearchCV\n\n# Load data\niris = datasets.load_iris()\nfeatures = iris.data\ntarget = iris.target\n\n# Create logistic regression\nlogistic = linear_model.LogisticRegression(max_iter=500, \n                                           solver=\"liblinear\")\n\n# Create range of candidate regularization penalty hyperparameter values\npenalty = [\"l1\", \"l2\"]\n\n# Create range of candidate values for C\nC = np.logspace(0, 4, 1000)\n\n# Create hyperparameter options\nhyperparameters = dict(C=C, penalty=penalty)\n\n# Create grid search\ngridsearch = GridSearchCV(logistic, hyperparameters, cv=5, n_jobs=-1, \n                             verbose=1)\n\n# Fit grid search\nbest_model = gridsearch.fit(features, target)\n\n# Print best model\nprint(best_model.best_estimator_)\n\n# Fitting 5 folds for each of 2000 candidates, totalling 10000 fits\n# LogisticRegression(C=5.926151812475554, max_iter=500, penalty='l1',\n                                                  solver=\"liblinear\")\n\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"6_%D8%A7%D9%81%D8%B2%D8%A7%DB%8C%D8%B4_%D8%B3%D8%B1%D8%B9%D8%AA_%D8%A7%D9%86%D8%AA%D8%AE%D8%A7%D8%A8_%D9%85%D8%AF%D9%84_%D8%B1%D9%88%D8%B4_%D9%87%D8%A7%DB%8C_%D8%AE%D8%A7%D8%B5_%D8%A7%D9%84%DA%AF%D9%88%D8%B1%DB%8C%D8%AA%D9%85\"><\/span>\n<p>  6. \u0627\u0641\u0632\u0627\u06cc\u0634 \u0633\u0631\u0639\u062a \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 (\u0631\u0648\u0634 \u0647\u0627\u06cc \u062e\u0627\u0635 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645).<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u06cc\u0646 \u0631\u0627\u0647\u06cc \u0628\u0631\u0627\u06cc \u0627\u0641\u0632\u0627\u06cc\u0634 \u0633\u0631\u0639\u062a \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u062f\u0644 \u0628\u062f\u0648\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0648\u0627\u0646 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0627\u0636\u0627\u0641\u06cc \u0627\u0633\u062a.<\/p>\n<p>\u0627\u06cc\u0646 \u0627\u0645\u06a9\u0627\u0646 \u067e\u0630\u06cc\u0631 \u0627\u0633\u062a \u0632\u06cc\u0631\u0627 scikit-learn \u062f\u0627\u0631\u0627\u06cc \u062a\u0646\u0638\u06cc\u0645 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u0627\u0639\u062a\u0628\u0627\u0631 \u0645\u062a\u0642\u0627\u0628\u0644 \u0645\u062f\u0644 \u062e\u0627\u0635 \u0627\u0633\u062a.<\/p>\n<p>\u06af\u0627\u0647\u06cc \u0627\u0648\u0642\u0627\u062a \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u06cc\u06a9 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0628\u0647 \u0645\u0627 \u0627\u0645\u06a9\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u062a\u0627 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0647\u0627\u06cc\u067e\u0631\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627 \u0631\u0627 \u0628\u0627 \u0633\u0631\u0639\u062a \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u0633\u0631\u06cc\u0639\u062a\u0631 \u062c\u0633\u062a\u062c\u0648 \u06a9\u0646\u06cc\u0645.<\/p>\n<p><strong>\u0645\u062b\u0627\u0644:<\/strong><br \/><strong>LogisticRegression<\/strong> \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u06cc\u06a9 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u06a9\u0646\u0646\u062f\u0647 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0644\u062c\u0633\u062a\u06cc\u06a9 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.<br \/><strong>LogisticRegressionCV<\/strong> \u06cc\u06a9 \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc\u200c\u06a9\u0646\u0646\u062f\u0647 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0644\u062c\u0633\u062a\u06cc\u06a9 \u0645\u0639\u062a\u0628\u0631 \u0645\u062a\u0642\u0627\u0637\u0639 \u06a9\u0627\u0631\u0622\u0645\u062f \u0631\u0627 \u067e\u06cc\u0627\u062f\u0647\u200c\u0633\u0627\u0632\u06cc \u0645\u06cc\u200c\u06a9\u0646\u062f \u06a9\u0647 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u0645\u0642\u062f\u0627\u0631 \u0628\u0647\u06cc\u0646\u0647 \u0627\u0628\u0631\u067e\u0627\u0631\u0627\u0645\u062a\u0631 C \u0631\u0627 \u0634\u0646\u0627\u0633\u0627\u06cc\u06cc \u06a9\u0646\u062f.<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight plaintext\"><code># Load libraries\nfrom sklearn import linear_model, datasets\n\n# Load data\niris = datasets.load_iris()\nfeatures = iris.data\ntarget = iris.target\n\n# Create cross-validated logistic regression\nlogit = linear_model.LogisticRegressionCV(Cs=100, max_iter=500,\n                                            solver=\"liblinear\")\n\n# Train model\nlogit.fit(features, target)\n\n# Print model\nprint(logit)\n\n# LogisticRegressionCV(Cs=100, max_iter=500, solver=\"liblinear\")\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p><strong>\u062a\u0648\u062c\u0647:<\/strong>\u06cc\u06a9 \u0646\u0642\u0637\u0647 \u0636\u0639\u0641 \u0639\u0645\u062f\u0647 \u0628\u0631\u0627\u06cc LogisticRegressionCV \u0627\u06cc\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0641\u0642\u0637 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0645\u062d\u062f\u0648\u062f\u0647 \u0627\u06cc \u0627\u0632 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0631\u0627 \u0628\u0631\u0627\u06cc C \u062c\u0633\u062a\u062c\u0648 \u06a9\u0646\u062f. \u0627\u06cc\u0646 \u0645\u062d\u062f\u0648\u062f\u06cc\u062a \u062f\u0631 \u0628\u0633\u06cc\u0627\u0631\u06cc \u0627\u0632 \u0631\u0648\u06cc\u06a9\u0631\u062f\u0647\u0627\u06cc \u0627\u0639\u062a\u0628\u0627\u0631\u0633\u0646\u062c\u06cc \u0645\u062a\u0642\u0627\u0628\u0644 \u0645\u062f\u0644 \u062e\u0627\u0635 scikit-learn \u0645\u0634\u062a\u0631\u06a9 \u0627\u0633\u062a.<\/p>\n<p>\u0627\u0645\u06cc\u062f\u0648\u0627\u0631\u0645 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u0627\u06cc\u062c\u0627\u062f \u06cc\u06a9 \u0646\u0645\u0627\u06cc \u06a9\u0644\u06cc \u0633\u0631\u06cc\u0639 \u0627\u0632 \u0646\u062d\u0648\u0647 \u0627\u0646\u062a\u062e\u0627\u0628 \u06cc\u06a9 \u0645\u062f\u0644 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0645\u0641\u06cc\u062f \u0628\u0648\u062f\u0647 \u0628\u0627\u0634\u062f.<\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Summarize this content to 400 words in Persian Lang 1. \u0645\u0642\u062f\u0645\u0647 \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u06cc\u0627\u062f \u0645\u06cc\u200c\u06af\u06cc\u0631\u06cc\u0645 \u06a9\u0647 \u0686\u06af\u0648\u0646\u0647 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644 \u0631\u0627 \u0628\u06cc\u0646 \u0686\u0646\u062f\u06cc\u0646 \u0645\u062f\u0644 \u0628\u0627 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u0646\u06cc\u0645\u060c \u062f\u0631 \u0628\u0631\u062e\u06cc \u0645\u0648\u0627\u0631\u062f \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0628\u06cc\u0634 \u0627\u0632 50 \u0645\u062f\u0644 \u0645\u062e\u062a\u0644\u0641 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u0645\u060c \u062f\u0627\u0646\u0633\u062a\u0646 \u0646\u062d\u0648\u0647 \u0627\u0646\u062a\u062e\u0627\u0628 \u06cc\u06a9\u06cc \u0628\u0631\u0627\u06cc \u0628\u0647 \u062f\u0633\u062a \u0622\u0648\u0631\u062f\u0646 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0639\u0645\u0644\u06a9\u0631\u062f \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0634\u0645\u0627 \u0645\u0647\u0645 &hellip;<\/p>\n","protected":false},"author":2,"featured_media":77891,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"","fifu_image_alt":"","footnotes":""},"categories":[339],"tags":[],"class_list":["post-77890","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-dev"],"_links":{"self":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/posts\/77890","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/comments?post=77890"}],"version-history":[{"count":0,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/posts\/77890\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/media\/77891"}],"wp:attachment":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/media?parent=77890"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/categories?post=77890"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/tags?post=77890"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}