{"id":66053,"date":"2024-06-10T22:41:20","date_gmt":"2024-06-10T19:11:20","guid":{"rendered":"https:\/\/nabfollower.com\/blog\/%d8%a7%d8%b5%d9%84%d8%a7%d8%ad-%d8%b7%d8%b1%d8%ad-%d8%ae%d9%84%d8%a7%d8%b5%d9%87-%da%86%d9%86%d8%af-%da%a9%d9%84%d8%a7%d8%b3%d9%87-shap-%da%a9%d8%a7%d9%87%d8%b4-%d8%a8%d9%87-%d9%86%d8%b3%d8%ae%d9%87\/"},"modified":"2024-06-10T22:41:20","modified_gmt":"2024-06-10T19:11:20","slug":"%d8%a7%d8%b5%d9%84%d8%a7%d8%ad-%d8%b7%d8%b1%d8%ad-%d8%ae%d9%84%d8%a7%d8%b5%d9%87-%da%86%d9%86%d8%af-%da%a9%d9%84%d8%a7%d8%b3%d9%87-shap-%da%a9%d8%a7%d9%87%d8%b4-%d8%a8%d9%87-%d9%86%d8%b3%d8%ae%d9%87","status":"publish","type":"post","link":"https:\/\/nabfollower.com\/blog\/%d8%a7%d8%b5%d9%84%d8%a7%d8%ad-%d8%b7%d8%b1%d8%ad-%d8%ae%d9%84%d8%a7%d8%b5%d9%87-%da%86%d9%86%d8%af-%da%a9%d9%84%d8%a7%d8%b3%d9%87-shap-%da%a9%d8%a7%d9%87%d8%b4-%d8%a8%d9%87-%d9%86%d8%b3%d8%ae%d9%87\/","title":{"rendered":"\u0627\u0635\u0644\u0627\u062d \u0637\u0631\u062d \u062e\u0644\u0627\u0635\u0647 \u0686\u0646\u062f \u06a9\u0644\u0627\u0633\u0647 SHAP – \u06a9\u0627\u0647\u0634 \u0628\u0647 \u0646\u0633\u062e\u0647 0.44.1 \u0627\u0632 0.45.0"},"content":{"rendered":"

<\/p>\n

\n

\u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u06cc\u06a9 \u0646\u06a9\u062a\u0647 \u0645\u0641\u06cc\u062f \u0628\u0631\u0627\u06cc \u0647\u0631 \u06a9\u0633\u06cc \u06a9\u0647 \u0627\u0632 #SHAP (\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0627\u0641\u0632\u0648\u062f\u0646\u06cc SHapley) \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u062f\u060c \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f:<\/p>\n

\u0627\u06af\u0631 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f shap.summary_plot(shap_values, X_train_tfidf_dense, plot_type=\"bar\")<\/code> \u0628\u0631\u0627\u06cc \u06cc\u06a9 \u062e\u0644\u0627\u0635\u0647 \u0686\u0646\u062f \u06a9\u0644\u0627\u0633\u0647\u060c \u0648 \u0628\u062f\u0648\u0646 \u062f\u0631 \u0646\u0638\u0631 \u06af\u0631\u0641\u062a\u0646 \u0646\u0648\u0639 \u0637\u0631\u062d\u06cc \u06a9\u0647 \u0639\u0628\u0648\u0631 \u0645\u06cc\u200c\u06a9\u0646\u06cc\u062f\u060c \u06cc\u06a9 \u0646\u0645\u0648\u062f\u0627\u0631 \u062a\u0639\u0627\u0645\u0644 \u0646\u0642\u0637\u0647\u200c\u062f\u0627\u0631 \u0631\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc\u200c\u062f\u0647\u062f. \u0628\u0647 \u0627\u062d\u062a\u0645\u0627\u0644 \u0632\u06cc\u0627\u062f \u06cc\u06a9 \u0627\u0634\u06a9\u0627\u0644 \u0627\u0633\u062a v0.45.0<\/code>.<\/p>\n

\u062a\u0646\u0632\u0644 \u0628\u0647 \u0646\u0633\u062e\u0647 0.44.1 \u0622\u0646 \u0631\u0627 \u0628\u0631\u0627\u06cc \u0645\u0646 \u0628\u0631\u0637\u0631\u0641 \u06a9\u0631\u062f. \u0627\u0645\u06cc\u062f\u0648\u0627\u0631\u0645 \u0627\u06cc\u0646 \u0628\u0627\u0639\u062b \u0635\u0631\u0641\u0647 \u062c\u0648\u06cc\u06cc \u062f\u0631 \u0648\u0642\u062a \u06a9\u0633\u06cc \u0634\u0648\u062f! \ud83d\udc4d #\u062a\u0648\u0636\u06cc\u062d \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc<\/p>\n\n

\u0628\u0631\u0627\u06cc \u06a9\u0633\u0627\u0646\u06cc \u06a9\u0647 \u0646\u0645\u06cc\u200c\u062f\u0627\u0646\u0646\u062f SHAP \u0686\u06cc\u0633\u062a\u060c \u0627\u06cc\u0646 \u0686\u0627\u0631\u0686\u0648\u0628\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u062a\u0648\u0636\u06cc\u062d \u062f\u0647\u062f \u06a9\u0647 \u0686\u0631\u0627 \u0645\u062f\u0644 #MachineLearning \u0634\u0645\u0627 \u0627\u06cc\u0646 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u062e\u0627\u0635 \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u062f\u0627\u062f\u0647 \u0648 \u0686\u0647 \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627\u06cc\u06cc \u062f\u0631 \u0622\u0646 \u062e\u0631\u0648\u062c\u06cc \u0646\u0642\u0634 \u062f\u0627\u0634\u062a\u0647 \u0627\u0633\u062a.<\/p>\n

SHAP (\u0627\u0634\u0627\u0631\u0647\u200c\u0647\u0627\u06cc \u0627\u0641\u0632\u0648\u062f\u0646\u06cc SHapley) \u06cc\u06a9 \u0631\u0648\u06cc\u06a9\u0631\u062f \u0646\u0638\u0631\u06cc \u0628\u0627\u0632\u06cc \u0628\u0631\u0627\u06cc \u062a\u0648\u0636\u06cc\u062d \u062e\u0631\u0648\u062c\u06cc \u0647\u0631 \u0645\u062f\u0644 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646\u06cc \u0627\u0633\u062a. \u0627\u06cc\u0646 \u062a\u062e\u0635\u06cc\u0635 \u0627\u0639\u062a\u0628\u0627\u0631 \u0628\u0647\u06cc\u0646\u0647 \u0631\u0627 \u0628\u0627 \u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0645\u062d\u0644\u06cc \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0645\u0642\u0627\u062f\u06cc\u0631 \u06a9\u0644\u0627\u0633\u06cc\u06a9 Shapley \u0627\u0632 \u062a\u0626\u0648\u0631\u06cc \u0628\u0627\u0632\u06cc \u0647\u0627 \u0648 \u067e\u0633\u0648\u0646\u062f\u0647\u0627\u06cc \u0645\u0631\u062a\u0628\u0637 \u0628\u0627 \u0622\u0646\u0647\u0627 \u0645\u0631\u062a\u0628\u0637 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n\n

\u062f\u0631 \u0632\u06cc\u0631 \u06cc\u06a9 \u06a9\u062f \u0633\u0627\u062f\u0647 \u0628\u0631\u0627\u06cc \u0646\u0634\u0627\u0646 \u062f\u0627\u062f\u0646 \u0645\u0634\u06a9\u0644 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f:<\/p>\n

\n
# Create a synthetic dataset\n<\/span>X<\/span>,<\/span> y<\/span> =<\/span> make_classification<\/span>(<\/span>n_samples<\/span>=<\/span>100<\/span>,<\/span> n_features<\/span>=<\/span>5<\/span>,<\/span> n_informative<\/span>=<\/span>3<\/span>,<\/span> n_redundant<\/span>=<\/span>1<\/span>,<\/span> n_clusters_per_class<\/span>=<\/span>1<\/span>,<\/span> n_classes<\/span>=<\/span>3<\/span>,<\/span> random_state<\/span>=<\/span>42<\/span>)<\/span>\nfeatures<\/span> =<\/span> [<\/span>f<\/span>\"<\/span>Feature <\/span>{<\/span>i<\/span>}<\/span>\"<\/span> for<\/span> i<\/span> in<\/span> range<\/span>(<\/span>X<\/span>.<\/span>shape<\/span>[<\/span>1<\/span>])]<\/span>\nX<\/span> =<\/span> pd<\/span>.<\/span>DataFrame<\/span>(<\/span>X<\/span>,<\/span> columns<\/span>=<\/span>features<\/span>)<\/span>\n\n# Train a RandomForest model\n<\/span>model<\/span> =<\/span> RandomForestClassifier<\/span>(<\/span>n_estimators<\/span>=<\/span>50<\/span>,<\/span> random_state<\/span>=<\/span>42<\/span>)<\/span>\nmodel<\/span>.<\/span>fit<\/span>(<\/span>X<\/span>,<\/span> y<\/span>)<\/span>\n\n# Create the SHAP Explainer\n<\/span>explainer<\/span> =<\/span> shap<\/span>.<\/span>TreeExplainer<\/span>(<\/span>model<\/span>)<\/span>\nshap_values<\/span> =<\/span> explainer<\/span>.<\/span>shap_values<\/span>(<\/span>X<\/span>)<\/span>\n\n# Plot SHAP values for each class\n<\/span>shap<\/span>.<\/span>summary_plot<\/span>(<\/span>shap_values<\/span>,<\/span> X<\/span>,<\/span> plot_type<\/span>=<\/span>\"<\/span>bar<\/span>\"<\/span>,<\/span> class_names<\/span>=<\/span>[<\/span>'<\/span>Class 0<\/span>'<\/span>,<\/span> '<\/span>Class 1<\/span>'<\/span>,<\/span> '<\/span>Class 2<\/span>'<\/span>])<\/span>\n<\/code><\/pre>\n
\n
\n    \u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n
    \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n\n
\u0627\u06cc\u0646 \u0647\u0645 \u0627\u0633\u06a9\u0631\u06cc\u0646 \u0634\u0627\u062a \u0647\u0627\u06cc \u0647\u0631 \u062f\u0648 \u0646\u0633\u062e\u0647:<\/p>\n
\"SHAP<\/p>\n
\"\u0646\u0645\u0648\u062f\u0627\u0631<\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"
\u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u06cc\u06a9 \u0646\u06a9\u062a\u0647 \u0645\u0641\u06cc\u062f \u0628\u0631\u0627\u06cc \u0647\u0631 \u06a9\u0633\u06cc \u06a9\u0647 \u0627\u0632 #SHAP (\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0627\u0641\u0632\u0648\u062f\u0646\u06cc SHapley) \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u062f\u060c \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f: \u0627\u06af\u0631 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f shap.summary_plot(shap_values, X_train_tfidf_dense, plot_type=”bar”) \u0628\u0631\u0627\u06cc \u06cc\u06a9 \u062e\u0644\u0627\u0635\u0647 \u0686\u0646\u062f \u06a9\u0644\u0627\u0633\u0647\u060c \u0648 \u0628\u062f\u0648\u0646 \u062f\u0631 \u0646\u0638\u0631 \u06af\u0631\u0641\u062a\u0646 \u0646\u0648\u0639 \u0637\u0631\u062d\u06cc \u06a9\u0647 \u0639\u0628\u0648\u0631 \u0645\u06cc\u200c\u06a9\u0646\u06cc\u062f\u060c \u06cc\u06a9 \u0646\u0645\u0648\u062f\u0627\u0631 \u062a\u0639\u0627\u0645\u0644 \u0646\u0642\u0637\u0647\u200c\u062f\u0627\u0631 \u0631\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc\u200c\u062f\u0647\u062f. \u0628\u0647 \u0627\u062d\u062a\u0645\u0627\u0644 \u0632\u06cc\u0627\u062f \u06cc\u06a9 \u0627\u0634\u06a9\u0627\u0644 \u0627\u0633\u062a …<\/p>\n","protected":false},"author":2,"featured_media":66054,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"https:\/\/media.dev.to\/cdn-cgi\/image\/width=1000,height=500,fit=cover,gravity=auto,format=auto\/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fjukpo9ymoup7p0ijj855.png","fifu_image_alt":"","footnotes":""},"categories":[339],"tags":[],"class_list":["post-66053","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\/66053","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=66053"}],"version-history":[{"count":0,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/posts\/66053\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/media\/66054"}],"wp:attachment":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/media?parent=66053"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/categories?post=66053"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/tags?post=66053"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}