{"id":83452,"date":"2024-11-12T03:04:48","date_gmt":"2024-11-11T23:34:48","guid":{"rendered":"https:\/\/nabfollower.com\/blog\/introduction-to-classification-in-machine-learning-3ge5\/"},"modified":"2024-11-12T03:04:48","modified_gmt":"2024-11-11T23:34:48","slug":"introduction-to-classification-in-machine-learning-3ge5","status":"publish","type":"post","link":"https:\/\/nabfollower.com\/blog\/introduction-to-classification-in-machine-learning-3ge5\/","title":{"rendered":"\u0645\u0642\u062f\u0645\u0647 \u0627\u06cc \u0628\u0631 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u062f\u0631 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646\u06cc"},"content":{"rendered":"<p>Summarize this content to 400 words in Persian Lang<br \/>\n            \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0686\u06cc\u0633\u062a\u061f\u062a\u0648\u0636\u06cc\u062d \u062f\u0647\u06cc\u062f \u06a9\u0647 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u06cc\u06a9 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\u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f).<\/p>\n<p>\u0627\u0635\u0637\u0644\u0627\u062d\u0627\u062a \u0648 \u0645\u0641\u0627\u0647\u06cc\u0645 \u067e\u0627\u06cc\u0647\u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0648 \u0628\u0631\u0686\u0633\u0628 \u0647\u0627: \u062a\u0648\u0636\u06cc\u062d \u062f\u0647\u06cc\u062f \u06a9\u0647 \u0686\u0647 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc\u06cc (\u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u0648\u0631\u0648\u062f\u06cc) \u0648 \u0628\u0631\u0686\u0633\u0628 \u0647\u0627 (\u0645\u062a\u063a\u06cc\u0631 \u062e\u0631\u0648\u062c\u06cc) \u0647\u0633\u062a\u0646\u062f.\u0622\u0645\u0648\u0632\u0634 \u0648 \u0622\u0632\u0645\u0627\u06cc\u0634: \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc\u060c \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u0648 \u0627\u0647\u0645\u06cc\u062a \u062a\u0642\u0633\u06cc\u0645 \u062f\u0627\u062f\u0647 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\u0628\u0631\u0627\u06cc \u06a9\u0644\u0627\u0633 \u0647\u0627\u06cc \u062e\u0627\u0635 \u06a9\u0645\u06a9 \u0645\u06cc \u06a9\u0646\u0646\u062f.\u0627\u0645\u062a\u06cc\u0627\u0632 F1: \u062f\u0642\u062a \u0648 \u06cc\u0627\u062f\u0622\u0648\u0631\u06cc \u0631\u0627 \u0645\u062a\u0639\u0627\u062f\u0644 \u0645\u06cc \u06a9\u0646\u062f\u060c \u0628\u0631\u0627\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0646\u0627\u0645\u062a\u0639\u0627\u062f\u0644 \u0645\u0641\u06cc\u062f \u0627\u0633\u062a.ROC-AUC: \u0628\u0631\u0627\u06cc \u0645\u0634\u06a9\u0644\u0627\u062a \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0628\u0627\u06cc\u0646\u0631\u06cc \u062e\u0648\u0628 \u0627\u0633\u062a.<\/p>\n<p>\u062a\u0646\u0638\u06cc\u0645 \u0645\u062d\u06cc\u0637\u0645\u0631\u0627\u062d\u0644 \u0646\u0635\u0628 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u0644\u0627\u0632\u0645 \u0631\u0627 \u0627\u0631\u0627\u0626\u0647 \u062f\u0647\u06cc\u062f (\u0645\u0627\u0646\u0646\u062f scikit-learn\u060c pandas\u060c numpy\u060c matplotlib).\u06a9\u062f \u0645\u062b\u0627\u0644 \u0628\u0631\u0627\u06cc \u0646\u0635\u0628 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627:<\/p>\n<p>!pip install scikit-learn pandas numpy matplotlib<\/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\u06a9 \u062f\u0627\u062f\u0647 \u0647\u0627\u0628\u0627\u0631\u06af\u06cc\u0631\u06cc \u062f\u0627\u062f\u0647: \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0646\u0645\u0648\u0646\u0647 (\u0645\u062b\u0644\u0627 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0639\u0631\u0648\u0641 Iris \u06cc\u0627 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0633\u0641\u0627\u0631\u0634\u06cc) \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u067e\u0627\u0646\u062f\u0627\u0647\u0627 \u0628\u0627\u0631\u06af\u06cc\u0631\u06cc \u06a9\u0646\u06cc\u062f.\u06a9\u0627\u0648\u0634 \u062f\u0627\u062f\u0647 \u0647\u0627: \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u060c \u06a9\u0644\u0627\u0633 \u0647\u0627\u06cc \u0647\u062f\u0641 \u0648 \u0634\u06a9\u0644 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0631\u0627 \u0634\u0631\u062d \u062f\u0647\u06cc\u062f.\u0628\u0631\u0627\u06cc \u062f\u0631\u06a9 \u062a\u0648\u0632\u06cc\u0639 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0648 \u0631\u0648\u0627\u0628\u0637\u060c \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u062a\u062c\u0633\u0645 \u06a9\u0646\u06cc\u062f.<\/p>\n<p>import pandas as pd<br \/>\nfrom sklearn.datasets import load_iris<\/p>\n<p># Load Iris dataset<br \/>\ndata = load_iris()<br \/>\ndf = pd.DataFrame(data.data, columns=data.feature_names)<br \/>\ndf[&#8216;target&#8217;] = data.target<br \/>\nprint(df.head())<\/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>\u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u062f\u0627\u062f\u0647 \u0647\u0627\u067e\u0627\u06a9\u0633\u0627\u0632\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627: \u0645\u0648\u0627\u0631\u062f \u062a\u06a9\u0631\u0627\u0631\u06cc \u0631\u0627 \u062d\u0630\u0641 \u06a9\u0646\u06cc\u062f\u060c \u0645\u0642\u0627\u062f\u06cc\u0631 \u0627\u0632 \u062f\u0633\u062a \u0631\u0641\u062a\u0647 \u0631\u0627 \u0645\u062f\u06cc\u0631\u06cc\u062a \u06a9\u0646\u06cc\u062f \u0648 \u063a\u06cc\u0631\u0647.\u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627: \u062f\u0631 \u0635\u0648\u0631\u062a \u0644\u0632\u0648\u0645 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0631\u0627 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u06cc\u0627 \u0639\u0627\u062f\u06cc \u06a9\u0646\u06cc\u062f (\u0628\u0647 \u0648\u06cc\u0698\u0647 \u0628\u0631\u0627\u06cc \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627\u06cc\u06cc \u0645\u0627\u0646\u0646\u062f SVM).\u062a\u0642\u0633\u06cc\u0645 \u062f\u0627\u062f\u0647 \u0647\u0627: \u0627\u0632 train_test_split \u0628\u0631\u0627\u06cc \u062a\u0642\u0633\u06cc\u0645 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0628\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f.<\/p>\n<p>from sklearn.model_selection import train_test_split<\/p>\n<p>X = df.drop(&#8216;target&#8217;, axis=1)<br \/>\ny = df[&#8216;target&#8217;]\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)<\/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>\u0627\u0646\u062a\u062e\u0627\u0628 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc\u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627\u06cc \u0645\u062d\u0628\u0648\u0628 \u0631\u0627 \u0645\u0639\u0631\u0641\u06cc \u06a9\u0646\u06cc\u062f \u0648 \u0628\u0647 \u0637\u0648\u0631 \u062e\u0644\u0627\u0635\u0647 \u062a\u0648\u0636\u06cc\u062d \u062f\u0647\u06cc\u062f \u06a9\u0647 \u0686\u0647 \u0632\u0645\u0627\u0646\u06cc \u0627\u0632 \u0647\u0631 \u06a9\u062f\u0627\u0645 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f:\u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0644\u062c\u0633\u062a\u06cc\u06a9: \u0628\u0631\u0627\u06cc \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0628\u0627\u06cc\u0646\u0631\u06cc \u0648 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0633\u0627\u062f\u0647 \u062e\u0648\u0628 \u0627\u0633\u062a.K-Nearest Neighbors (KNN): \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u06a9\u0648\u0686\u06a9 \u0645\u0648\u062b\u0631 \u0627\u0633\u062a \u0648 \u0628\u0647 \u0631\u0627\u062d\u062a\u06cc \u0642\u0627\u0628\u0644 \u062a\u0641\u0633\u06cc\u0631 \u0627\u0633\u062a.\u062f\u0631\u062e\u062a\u0627\u0646 \u062a\u0635\u0645\u06cc\u0645: \u062a\u062c\u0633\u0645 \u0622\u0633\u0627\u0646\u060c \u0631\u0648\u0627\u0628\u0637 \u063a\u06cc\u0631 \u062e\u0637\u06cc \u0631\u0627 \u0645\u062f\u06cc\u0631\u06cc\u062a \u0645\u06cc \u06a9\u0646\u062f.Random Forest: \u062a\u06a9\u0646\u06cc\u06a9 Ensemble\u060c \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0628\u0631\u0627\u0632\u0634 \u0631\u0627 \u062f\u0631 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0628\u0627 Decision Trees \u06a9\u0627\u0647\u0634 \u0645\u06cc \u062f\u0647\u062f.\u0645\u0627\u0634\u06cc\u0646 \u0628\u0631\u062f\u0627\u0631 \u067e\u0634\u062a\u06cc\u0628\u0627\u0646\u06cc (SVM): \u0628\u0631\u0627\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0628\u0627 \u0627\u0628\u0639\u0627\u062f \u0628\u0627\u0644\u0627 \u0645\u0648\u062b\u0631 \u0627\u0633\u062a\u060c \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0646\u06cc\u0627\u0632 \u0628\u0647 \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f.\u0633\u0627\u062f\u0647 \u0628\u06cc\u0632: \u0628\u0631 \u0627\u0633\u0627\u0633 \u0642\u0636\u06cc\u0647 \u0628\u06cc\u0632\u060c \u0628\u0631\u0627\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0645\u062a\u0646\u06cc \u0648 \u062a\u0641\u0633\u06cc\u0631 \u0627\u062d\u062a\u0645\u0627\u0644\u06cc \u062e\u0648\u0628 \u0627\u0633\u062a.\u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644\u0646\u0645\u0648\u0646\u0647 \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644: \u06cc\u06a9 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 (\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0644\u062c\u0633\u062a\u06cc\u06a9) \u0631\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u0646\u06cc\u062f \u0648 \u0622\u0646 \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u062f.\u06a9\u062f \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0631\u0627 \u0627\u0631\u0627\u0626\u0647 \u062f\u0647\u06cc\u062f.<\/p>\n<p>from sklearn.linear_model import LogisticRegression<\/p>\n<p>model = LogisticRegression()<br \/>\nmodel.fit(X_train, y_train)<\/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>\u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0648 \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0645\u062f\u0644\u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u0647\u0627: \u0646\u062d\u0648\u0647 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0645\u062f\u0644 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647 \u0646\u0634\u0627\u0646 \u062f\u0647\u06cc\u062f.\u0627\u0631\u0632\u06cc\u0627\u0628\u06cc: \u062f\u0642\u062a\u060c \u062f\u0642\u062a\u060c \u06cc\u0627\u062f\u0622\u0648\u0631\u06cc\u060c \u0627\u0645\u062a\u06cc\u0627\u0632 F1 \u0648 \u0645\u0627\u062a\u0631\u06cc\u0633 \u0633\u0631\u062f\u0631\u06af\u0645\u06cc \u0631\u0627 \u0645\u062d\u0627\u0633\u0628\u0647 \u06a9\u0646\u06cc\u062f.\u062a\u062c\u0633\u0645: \u062f\u0631 \u0635\u0648\u0631\u062a \u0648\u062c\u0648\u062f\u060c \u0645\u0627\u062a\u0631\u06cc\u0633 \u0633\u0631\u062f\u0631\u06af\u0645\u06cc \u0648\/\u06cc\u0627 \u0645\u0646\u062d\u0646\u06cc ROC \u0631\u0627 \u0631\u0633\u0645 \u06a9\u0646\u06cc\u062f.<\/p>\n<p>from sklearn.metrics import accuracy_score, confusion_matrix, classification_report<\/p>\n<p>y_pred = model.predict(X_test)<br \/>\nprint(f&#8217;Accuracy: {accuracy_score(y_test, y_pred)}&#8217;)<br \/>\nprint(confusion_matrix(y_test, y_pred))<br \/>\nprint(classification_report(y_test, y_pred))<\/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\u0646\u0638\u06cc\u0645 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0627\u0647\u0645\u06cc\u062a \u062a\u0646\u0638\u06cc\u0645 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627 \u0628\u0631\u0627\u06cc \u0628\u0647\u0628\u0648\u062f \u0639\u0645\u0644\u06a9\u0631\u062f \u0645\u062f\u0644 \u0631\u0627 \u062a\u0648\u0636\u06cc\u062d \u062f\u0647\u06cc\u062f.Grid Search \u0648 Random Search: GridSearchCV \u0648 RandomizedSearchCV \u0631\u0627 \u0645\u0639\u0631\u0641\u06cc \u06a9\u0646\u06cc\u062f.\u06a9\u062f \u0645\u062b\u0627\u0644\u06cc \u0628\u0631\u0627\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 GridSearchCV \u0628\u0631\u0627\u06cc \u062a\u0646\u0638\u06cc\u0645 \u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u0627\u0631\u0627\u0626\u0647 \u062f\u0647\u06cc\u062f.<\/p>\n<p>from sklearn.model_selection import GridSearchCV<\/p>\n<p>param_grid = {&#8216;C&#8217;: [0.1, 1, 10], &#8216;solver&#8217;: [&#8216;lbfgs&#8217;, &#8216;liblinear&#8217;]}<br \/>\ngrid = GridSearchCV(LogisticRegression(), param_grid, refit=True)<br \/>\ngrid.fit(X_train, y_train)<br \/>\nprint(grid.best_params_)<\/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>\u0622\u0632\u0645\u0627\u06cc\u0634 \u0628\u0631 \u0631\u0648\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062c\u062f\u06cc\u062f \u0648 \u0646\u062a\u06cc\u062c\u0647 \u06af\u06cc\u0631\u06cc\u0628\u0631 \u0627\u0647\u0645\u06cc\u062a \u0622\u0632\u0645\u0627\u06cc\u0634 \u0645\u062f\u0644 \u0628\u0631 \u0631\u0648\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062c\u062f\u06cc\u062f \u0628\u0631\u0627\u06cc \u062c\u0644\u0648\u06af\u06cc\u0631\u06cc \u0627\u0632 \u0628\u0631\u0627\u0632\u0634 \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u062a\u0623\u06a9\u06cc\u062f \u06a9\u0646\u06cc\u062f.\u0646\u06a9\u0627\u062a \u06a9\u0644\u06cc\u062f\u06cc \u0631\u0627 \u062e\u0644\u0627\u0635\u0647 \u06a9\u0646\u06cc\u062f \u0648 \u0645\u0646\u0627\u0628\u0639 \u0627\u0636\u0627\u0641\u06cc \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0628\u06cc\u0634\u062a\u0631 \u0641\u0631\u0627\u0647\u0645 \u06a9\u0646\u06cc\u062f.\u067e\u06cc\u0648\u0646\u062f\u0647\u0627\u06cc\u06cc \u0628\u0647 \u0645\u0646\u0627\u0628\u0639 \u0645\u0641\u06cc\u062f\u060c \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647\u200c\u0647\u0627 \u0648 \u062e\u0648\u0627\u0646\u062f\u0646\u06cc\u200c\u0647\u0627\u06cc \u0627\u0636\u0627\u0641\u06cc \u0627\u0636\u0627\u0641\u0647 \u06a9\u0646\u06cc\u062f.\u0646\u0645\u0648\u0646\u0647 \u06a9\u062f \u06a9\u0627\u0645\u0644\u0628\u0631\u0627\u06cc \u0627\u0631\u062c\u0627\u0639 \u0633\u0631\u06cc\u0639\u060c \u06cc\u06a9 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u062a\u0644\u0641\u06cc\u0642\u06cc \u0628\u0627 \u062a\u0645\u0627\u0645 \u06a9\u062f\u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0627\u0631\u0627\u0626\u0647 \u06a9\u0646\u06cc\u062f.<\/p>\n<div data-article-id=\"2096923\" id=\"article-body\">\n<p><strong>\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0686\u06cc\u0633\u062a\u061f<\/strong><br \/>\u062a\u0648\u0636\u06cc\u062d \u062f\u0647\u06cc\u062f \u06a9\u0647 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u06cc\u06a9 \u062a\u06a9\u0646\u06cc\u06a9 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062a\u062d\u062a \u0646\u0638\u0627\u0631\u062a \u0627\u0633\u062a \u06a9\u0647 \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062f\u0633\u062a\u0647 \u0647\u0627 \u06cc\u0627 \u0628\u0631\u0686\u0633\u0628 \u0647\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.<br \/>\u0646\u0645\u0648\u0646\u0647 \u0647\u0627\u06cc\u06cc \u0627\u0632 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc: \u0634\u0646\u0627\u0633\u0627\u06cc\u06cc \u0627\u06cc\u0645\u06cc\u0644 \u0647\u0627\u06cc \u0627\u0633\u067e\u0645\u060c \u062a\u0634\u062e\u06cc\u0635 \u062a\u0635\u0648\u06cc\u0631\u060c \u062a\u0634\u062e\u06cc\u0635 \u0628\u06cc\u0645\u0627\u0631\u06cc \u0648 \u063a\u06cc\u0631\u0647.<br \/>\u0627\u0646\u0648\u0627\u0639 \u0645\u0633\u0627\u0626\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc<br \/>\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0628\u0627\u06cc\u0646\u0631\u06cc (\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u0628\u0644\u0647\/\u062e\u06cc\u0631\u060c \u0647\u0631\u0632\u0646\u0627\u0645\u0647\/\u0646\u0647 \u0647\u0631\u0632\u0646\u0627\u0645\u0647).<br \/>\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0686\u0646\u062f \u0637\u0628\u0642\u0647 (\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u062d\u06cc\u0648\u0627\u0646\u0627\u062a \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u06af\u0631\u0628\u0647\u060c \u0633\u06af\u060c \u06cc\u0627 \u067e\u0631\u0646\u062f\u0647).<br \/>\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc (\u0632\u0645\u0627\u0646\u06cc \u06a9\u0647 \u06cc\u06a9 \u0646\u0645\u0648\u0646\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0628\u0647 \u0686\u0646\u062f\u06cc\u0646 \u06a9\u0644\u0627\u0633 \u062a\u0639\u0644\u0642 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f).<\/p>\n<p><strong>\u0627\u0635\u0637\u0644\u0627\u062d\u0627\u062a \u0648 \u0645\u0641\u0627\u0647\u06cc\u0645 \u067e\u0627\u06cc\u0647<\/strong><br \/>\u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0648 \u0628\u0631\u0686\u0633\u0628 \u0647\u0627: \u062a\u0648\u0636\u06cc\u062d \u062f\u0647\u06cc\u062f \u06a9\u0647 \u0686\u0647 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc\u06cc (\u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u0648\u0631\u0648\u062f\u06cc) \u0648 \u0628\u0631\u0686\u0633\u0628 \u0647\u0627 (\u0645\u062a\u063a\u06cc\u0631 \u062e\u0631\u0648\u062c\u06cc) \u0647\u0633\u062a\u0646\u062f.<br \/>\u0622\u0645\u0648\u0632\u0634 \u0648 \u0622\u0632\u0645\u0627\u06cc\u0634: \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc\u060c \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u0648 \u0627\u0647\u0645\u06cc\u062a \u062a\u0642\u0633\u06cc\u0645 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0631\u0627 \u062a\u0639\u0631\u06cc\u0641 \u06a9\u0646\u06cc\u062f.<br \/>\u0645\u0639\u06cc\u0627\u0631\u0647\u0627\u06cc \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc: \u0645\u0639\u06cc\u0627\u0631\u0647\u0627\u06cc \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0631\u0627\u06cc\u062c \u0631\u0627 \u0628\u0631\u0627\u06cc \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u0639\u0631\u0641\u06cc \u06a9\u0646\u06cc\u062f:<br \/>\u062f\u0642\u062a: \u0647\u0631 \u0686\u0646\u062f \u0648\u0642\u062a \u06cc\u06a9\u0628\u0627\u0631 \u0645\u062f\u0644 \u0635\u062d\u06cc\u062d \u0627\u0633\u062a.<br \/>\u062f\u0642\u062a \u0648 \u06cc\u0627\u062f\u0622\u0648\u0631\u06cc: \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0646\u0627\u0645\u062a\u0639\u0627\u062f\u0644\u060c \u0627\u06cc\u0646 \u0645\u0639\u06cc\u0627\u0631\u0647\u0627 \u0628\u0647 \u0627\u0646\u062f\u0627\u0632\u0647 \u06af\u06cc\u0631\u06cc \u0635\u062d\u062a \u0628\u0631\u0627\u06cc \u06a9\u0644\u0627\u0633 \u0647\u0627\u06cc \u062e\u0627\u0635 \u06a9\u0645\u06a9 \u0645\u06cc \u06a9\u0646\u0646\u062f.<br \/>\u0627\u0645\u062a\u06cc\u0627\u0632 F1: \u062f\u0642\u062a \u0648 \u06cc\u0627\u062f\u0622\u0648\u0631\u06cc \u0631\u0627 \u0645\u062a\u0639\u0627\u062f\u0644 \u0645\u06cc \u06a9\u0646\u062f\u060c \u0628\u0631\u0627\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0646\u0627\u0645\u062a\u0639\u0627\u062f\u0644 \u0645\u0641\u06cc\u062f \u0627\u0633\u062a.<br \/>ROC-AUC: \u0628\u0631\u0627\u06cc \u0645\u0634\u06a9\u0644\u0627\u062a \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0628\u0627\u06cc\u0646\u0631\u06cc \u062e\u0648\u0628 \u0627\u0633\u062a.<\/p>\n<p><strong>\u062a\u0646\u0638\u06cc\u0645 \u0645\u062d\u06cc\u0637<\/strong><br \/>\u0645\u0631\u0627\u062d\u0644 \u0646\u0635\u0628 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u0644\u0627\u0632\u0645 \u0631\u0627 \u0627\u0631\u0627\u0626\u0647 \u062f\u0647\u06cc\u062f (\u0645\u0627\u0646\u0646\u062f scikit-learn\u060c pandas\u060c numpy\u060c matplotlib).<br \/>\u06a9\u062f \u0645\u062b\u0627\u0644 \u0628\u0631\u0627\u06cc \u0646\u0635\u0628 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627:<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight plaintext\"><code>!pip install scikit-learn pandas numpy matplotlib\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>\u062f\u0631\u06a9 \u062f\u0627\u062f\u0647 \u0647\u0627<br \/>\u0628\u0627\u0631\u06af\u06cc\u0631\u06cc \u062f\u0627\u062f\u0647: \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0646\u0645\u0648\u0646\u0647 (\u0645\u062b\u0644\u0627 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0639\u0631\u0648\u0641 Iris \u06cc\u0627 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0633\u0641\u0627\u0631\u0634\u06cc) \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u067e\u0627\u0646\u062f\u0627\u0647\u0627 \u0628\u0627\u0631\u06af\u06cc\u0631\u06cc \u06a9\u0646\u06cc\u062f.<br \/>\u06a9\u0627\u0648\u0634 \u062f\u0627\u062f\u0647 \u0647\u0627: \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u060c \u06a9\u0644\u0627\u0633 \u0647\u0627\u06cc \u0647\u062f\u0641 \u0648 \u0634\u06a9\u0644 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0631\u0627 \u0634\u0631\u062d \u062f\u0647\u06cc\u062f.<br \/>\u0628\u0631\u0627\u06cc \u062f\u0631\u06a9 \u062a\u0648\u0632\u06cc\u0639 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0648 \u0631\u0648\u0627\u0628\u0637\u060c \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u062a\u062c\u0633\u0645 \u06a9\u0646\u06cc\u062f.<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight plaintext\"><code>import pandas as pd\nfrom sklearn.datasets import load_iris\n\n# Load Iris dataset\ndata = load_iris()\ndf = pd.DataFrame(data.data, columns=data.feature_names)\ndf['target'] = data.target\nprint(df.head())\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>\u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u062f\u0627\u062f\u0647 \u0647\u0627<\/strong><br \/>\u067e\u0627\u06a9\u0633\u0627\u0632\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627: \u0645\u0648\u0627\u0631\u062f \u062a\u06a9\u0631\u0627\u0631\u06cc \u0631\u0627 \u062d\u0630\u0641 \u06a9\u0646\u06cc\u062f\u060c \u0645\u0642\u0627\u062f\u06cc\u0631 \u0627\u0632 \u062f\u0633\u062a \u0631\u0641\u062a\u0647 \u0631\u0627 \u0645\u062f\u06cc\u0631\u06cc\u062a \u06a9\u0646\u06cc\u062f \u0648 \u063a\u06cc\u0631\u0647.<br \/>\u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627: \u062f\u0631 \u0635\u0648\u0631\u062a \u0644\u0632\u0648\u0645 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0631\u0627 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u06cc\u0627 \u0639\u0627\u062f\u06cc \u06a9\u0646\u06cc\u062f (\u0628\u0647 \u0648\u06cc\u0698\u0647 \u0628\u0631\u0627\u06cc \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627\u06cc\u06cc \u0645\u0627\u0646\u0646\u062f SVM).<br \/>\u062a\u0642\u0633\u06cc\u0645 \u062f\u0627\u062f\u0647 \u0647\u0627: \u0627\u0632 train_test_split \u0628\u0631\u0627\u06cc \u062a\u0642\u0633\u06cc\u0645 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0628\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f.<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight plaintext\"><code>from sklearn.model_selection import train_test_split\n\nX = df.drop('target', axis=1)\ny = df['target']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\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>\u0627\u0646\u062a\u062e\u0627\u0628 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc<\/strong><br \/>\u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627\u06cc \u0645\u062d\u0628\u0648\u0628 \u0631\u0627 \u0645\u0639\u0631\u0641\u06cc \u06a9\u0646\u06cc\u062f \u0648 \u0628\u0647 \u0637\u0648\u0631 \u062e\u0644\u0627\u0635\u0647 \u062a\u0648\u0636\u06cc\u062d \u062f\u0647\u06cc\u062f \u06a9\u0647 \u0686\u0647 \u0632\u0645\u0627\u0646\u06cc \u0627\u0632 \u0647\u0631 \u06a9\u062f\u0627\u0645 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f:<br \/>\u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0644\u062c\u0633\u062a\u06cc\u06a9: \u0628\u0631\u0627\u06cc \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0628\u0627\u06cc\u0646\u0631\u06cc \u0648 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0633\u0627\u062f\u0647 \u062e\u0648\u0628 \u0627\u0633\u062a.<br \/>K-Nearest Neighbors (KNN): \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u06a9\u0648\u0686\u06a9 \u0645\u0648\u062b\u0631 \u0627\u0633\u062a \u0648 \u0628\u0647 \u0631\u0627\u062d\u062a\u06cc \u0642\u0627\u0628\u0644 \u062a\u0641\u0633\u06cc\u0631 \u0627\u0633\u062a.<br \/>\u062f\u0631\u062e\u062a\u0627\u0646 \u062a\u0635\u0645\u06cc\u0645: \u062a\u062c\u0633\u0645 \u0622\u0633\u0627\u0646\u060c \u0631\u0648\u0627\u0628\u0637 \u063a\u06cc\u0631 \u062e\u0637\u06cc \u0631\u0627 \u0645\u062f\u06cc\u0631\u06cc\u062a \u0645\u06cc \u06a9\u0646\u062f.<br \/>Random Forest: \u062a\u06a9\u0646\u06cc\u06a9 Ensemble\u060c \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0628\u0631\u0627\u0632\u0634 \u0631\u0627 \u062f\u0631 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0628\u0627 Decision Trees \u06a9\u0627\u0647\u0634 \u0645\u06cc \u062f\u0647\u062f.<br \/>\u0645\u0627\u0634\u06cc\u0646 \u0628\u0631\u062f\u0627\u0631 \u067e\u0634\u062a\u06cc\u0628\u0627\u0646\u06cc (SVM): \u0628\u0631\u0627\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0628\u0627 \u0627\u0628\u0639\u0627\u062f \u0628\u0627\u0644\u0627 \u0645\u0648\u062b\u0631 \u0627\u0633\u062a\u060c \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0646\u06cc\u0627\u0632 \u0628\u0647 \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f.<br \/>\u0633\u0627\u062f\u0647 \u0628\u06cc\u0632: \u0628\u0631 \u0627\u0633\u0627\u0633 \u0642\u0636\u06cc\u0647 \u0628\u06cc\u0632\u060c \u0628\u0631\u0627\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0645\u062a\u0646\u06cc \u0648 \u062a\u0641\u0633\u06cc\u0631 \u0627\u062d\u062a\u0645\u0627\u0644\u06cc \u062e\u0648\u0628 \u0627\u0633\u062a.<br \/>\u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644<br \/>\u0646\u0645\u0648\u0646\u0647 \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644: \u06cc\u06a9 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 (\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0644\u062c\u0633\u062a\u06cc\u06a9) \u0631\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u0646\u06cc\u062f \u0648 \u0622\u0646 \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u062f.<br \/>\u06a9\u062f \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0631\u0627 \u0627\u0631\u0627\u0626\u0647 \u062f\u0647\u06cc\u062f.<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight plaintext\"><code>from sklearn.linear_model import LogisticRegression\n\nmodel = LogisticRegression()\nmodel.fit(X_train, y_train)\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>\u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0648 \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0645\u062f\u0644<br \/>\u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u0647\u0627: \u0646\u062d\u0648\u0647 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0645\u062f\u0644 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647 \u0646\u0634\u0627\u0646 \u062f\u0647\u06cc\u062f.<br \/>\u0627\u0631\u0632\u06cc\u0627\u0628\u06cc: \u062f\u0642\u062a\u060c \u062f\u0642\u062a\u060c \u06cc\u0627\u062f\u0622\u0648\u0631\u06cc\u060c \u0627\u0645\u062a\u06cc\u0627\u0632 F1 \u0648 \u0645\u0627\u062a\u0631\u06cc\u0633 \u0633\u0631\u062f\u0631\u06af\u0645\u06cc \u0631\u0627 \u0645\u062d\u0627\u0633\u0628\u0647 \u06a9\u0646\u06cc\u062f.<br \/>\u062a\u062c\u0633\u0645: \u062f\u0631 \u0635\u0648\u0631\u062a \u0648\u062c\u0648\u062f\u060c \u0645\u0627\u062a\u0631\u06cc\u0633 \u0633\u0631\u062f\u0631\u06af\u0645\u06cc \u0648\/\u06cc\u0627 \u0645\u0646\u062d\u0646\u06cc ROC \u0631\u0627 \u0631\u0633\u0645 \u06a9\u0646\u06cc\u062f.<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight plaintext\"><code>from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n\ny_pred = model.predict(X_test)\nprint(f'Accuracy: {accuracy_score(y_test, y_pred)}')\nprint(confusion_matrix(y_test, y_pred))\nprint(classification_report(y_test, y_pred))\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\u0646\u0638\u06cc\u0645 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631<\/strong><br \/>\u0627\u0647\u0645\u06cc\u062a \u062a\u0646\u0638\u06cc\u0645 \u0641\u0631\u0627\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627 \u0628\u0631\u0627\u06cc \u0628\u0647\u0628\u0648\u062f \u0639\u0645\u0644\u06a9\u0631\u062f \u0645\u062f\u0644 \u0631\u0627 \u062a\u0648\u0636\u06cc\u062d \u062f\u0647\u06cc\u062f.<br \/>Grid Search \u0648 Random Search: GridSearchCV \u0648 RandomizedSearchCV \u0631\u0627 \u0645\u0639\u0631\u0641\u06cc \u06a9\u0646\u06cc\u062f.<br \/>\u06a9\u062f \u0645\u062b\u0627\u0644\u06cc \u0628\u0631\u0627\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 GridSearchCV \u0628\u0631\u0627\u06cc \u062a\u0646\u0638\u06cc\u0645 \u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u0627\u0631\u0627\u0626\u0647 \u062f\u0647\u06cc\u062f.<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight plaintext\"><code>from sklearn.model_selection import GridSearchCV\n\nparam_grid = {'C': [0.1, 1, 10], 'solver': ['lbfgs', 'liblinear']}\ngrid = GridSearchCV(LogisticRegression(), param_grid, refit=True)\ngrid.fit(X_train, y_train)\nprint(grid.best_params_)\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>\u0622\u0632\u0645\u0627\u06cc\u0634 \u0628\u0631 \u0631\u0648\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062c\u062f\u06cc\u062f \u0648 \u0646\u062a\u06cc\u062c\u0647 \u06af\u06cc\u0631\u06cc<\/strong><br \/>\u0628\u0631 \u0627\u0647\u0645\u06cc\u062a \u0622\u0632\u0645\u0627\u06cc\u0634 \u0645\u062f\u0644 \u0628\u0631 \u0631\u0648\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062c\u062f\u06cc\u062f \u0628\u0631\u0627\u06cc \u062c\u0644\u0648\u06af\u06cc\u0631\u06cc \u0627\u0632 \u0628\u0631\u0627\u0632\u0634 \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u062a\u0623\u06a9\u06cc\u062f \u06a9\u0646\u06cc\u062f.<br \/>\u0646\u06a9\u0627\u062a \u06a9\u0644\u06cc\u062f\u06cc \u0631\u0627 \u062e\u0644\u0627\u0635\u0647 \u06a9\u0646\u06cc\u062f \u0648 \u0645\u0646\u0627\u0628\u0639 \u0627\u0636\u0627\u0641\u06cc \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0628\u06cc\u0634\u062a\u0631 \u0641\u0631\u0627\u0647\u0645 \u06a9\u0646\u06cc\u062f.<br \/>\u067e\u06cc\u0648\u0646\u062f\u0647\u0627\u06cc\u06cc \u0628\u0647 \u0645\u0646\u0627\u0628\u0639 \u0645\u0641\u06cc\u062f\u060c \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647\u200c\u0647\u0627 \u0648 \u062e\u0648\u0627\u0646\u062f\u0646\u06cc\u200c\u0647\u0627\u06cc \u0627\u0636\u0627\u0641\u06cc \u0627\u0636\u0627\u0641\u0647 \u06a9\u0646\u06cc\u062f.<br \/>\u0646\u0645\u0648\u0646\u0647 \u06a9\u062f \u06a9\u0627\u0645\u0644<br \/>\u0628\u0631\u0627\u06cc \u0627\u0631\u062c\u0627\u0639 \u0633\u0631\u06cc\u0639\u060c \u06cc\u06a9 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u062a\u0644\u0641\u06cc\u0642\u06cc \u0628\u0627 \u062a\u0645\u0627\u0645 \u06a9\u062f\u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0627\u0631\u0627\u0626\u0647 \u06a9\u0646\u06cc\u062f.<\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Summarize this content to 400 words in Persian Lang \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0686\u06cc\u0633\u062a\u061f\u062a\u0648\u0636\u06cc\u062d \u062f\u0647\u06cc\u062f \u06a9\u0647 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u06cc\u06a9 \u062a\u06a9\u0646\u06cc\u06a9 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062a\u062d\u062a \u0646\u0638\u0627\u0631\u062a \u0627\u0633\u062a \u06a9\u0647 \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062f\u0633\u062a\u0647 \u0647\u0627 \u06cc\u0627 \u0628\u0631\u0686\u0633\u0628 \u0647\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.\u0646\u0645\u0648\u0646\u0647 \u0647\u0627\u06cc\u06cc \u0627\u0632 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc: \u0634\u0646\u0627\u0633\u0627\u06cc\u06cc \u0627\u06cc\u0645\u06cc\u0644 \u0647\u0627\u06cc \u0627\u0633\u067e\u0645\u060c \u062a\u0634\u062e\u06cc\u0635 \u062a\u0635\u0648\u06cc\u0631\u060c \u062a\u0634\u062e\u06cc\u0635 \u0628\u06cc\u0645\u0627\u0631\u06cc \u0648 \u063a\u06cc\u0631\u0647.\u0627\u0646\u0648\u0627\u0639 \u0645\u0633\u0627\u0626\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0628\u0627\u06cc\u0646\u0631\u06cc (\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 &hellip;<\/p>\n","protected":false},"author":2,"featured_media":83453,"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-83452","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\/83452","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=83452"}],"version-history":[{"count":0,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/posts\/83452\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/media\/83453"}],"wp:attachment":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/media?parent=83452"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/categories?post=83452"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/tags?post=83452"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}