model = CatBoostClassifier(verbose=False)model.fit(X_train, y_train)#Print scores for Multiclassy_test_pred = model.predict(X_test)y_test_prob = model.predict_proba(X_test)print(metrics.classification_report(y_test, y_test_pred, digits=3))print('Accuracy score: ', accuracy_score(y_test...
y_test_pred = model.predict(X_test) y_test_prob = model.predict_proba(X_test) print(metrics.classification_report(y_test, y_test_pred, digits=3)) print('Accuracy score: ', accuracy_score(y_test, y_test_pred)) print('Roc auc score : ', roc_auc_score(y_test, y_test_prob, mu...
plt.plot([0, 1], [0, 1], "k--", label="ROC curve for chance level (AUC = 0.5)") plt.axis("square") plt.xlabel("False Positive Rate") plt.ylabel("True Positive Rate") plt.title("Extension of Receiver Operating Characteristic\nto One-vs-Rest multiclass") plt.legend() plt.show...
plt.plot([0, 1], [0, 1], "k--", label="ROC curve for chance level (AUC = 0.5)") plt.axis("square") plt.xlabel("False Positive Rate") plt.ylabel("True Positive Rate") plt.title("Extension of Receiver Operating Characteristic\nto One-vs-Rest multiclass") plt.legend() plt.show...
#Print scores for Multiclass y_test_pred = model.predict(X_test) y_test_prob = model.predict_proba(X_test) print(metrics.classification_report(y_test, y_test_pred, digits=3)) print('Accuracy score: ', accuracy_score(y_test, y_test_pred)) ...
#Print scores for Multiclass y_test_pred = model.predict(X_test) y_test_prob = model.predict_proba(X_test) print(metrics.classification_report(y_test, y_test_pred, digits=3)) print('Accuracy score: ', accuracy_score(y_test, y_test_pred)) ...
#PrintscoresforMulticlass y_test_pred = model.predict(X_test) y_test_prob = model.predict_proba(X_test)print(metrics.classification_report(y_test, y_test_pred,digits=3))print('Accuracy score: ', accuracy_score(y_test, y_test_pred))print('Roc auc score : ', roc_auc_score(y_test,...
#Print scores for Multiclass y_test_pred = model.predict(X_test) y_test_prob = model.predict_proba(X_test) print(metrics.classification_report(y_test, y_test_pred, digits=3)) print('Accuracy score: ', accuracy_score(y_test, y_test_pred)) ...
for multiclass classification: 'logloss', 'f1', or 'accuracy', for regression: 'rmse', 'mse', 'mae', 'r2', 'mape', 'spearman', or 'pearson'. You can easily select evaluation metric by settingeval_metricinAutoML()constructor:
float32), y) catboost_onnx = convert_catboost(catboost_model, name='CatBoostMultiClassification', doc_string='test multiclass classification') self.assertTrue(catboost_onnx is not None) dump_data_and_model(X.astype(numpy.float32), catboost_model, catboost_onnx, basename="CatBoostMultiClass")...