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)) ...
它是腾讯开源的文本分类项目,是可以快速实现分层多标签分类任务的神经模型。...Binary-class text classifcation:二分类任务Multi-class text classification:多分类任务Multi-label text classification...:多标签任务Hiearchical (multi-label) text classification (HMC):多层多标签任务项目的整体框架项目的整体架构如下...
#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,...
#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,...
classes_count : int, [default=None] The upper limit for the numeric class label. Defines the number of classes for multiclassification. Only non-negative integers can be specified. The given integer should be greater than any of the target values. If this parameter is specified the labels ...