print(clf.predict(X)) clf.predict_proba(X) #这个是得分,每个分类器的得分,取最大得分对应的类。 #画图 plot_step=0.02 x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = n
x_test = np.array([[2,3,5],[4,7,3],[5,6,7]]) clf = svm.SVC(probability=True) clf.fit(x_train, y_train) # 返回预测标签 print("x_test所属的类别标签:",clf.predict(x_test)) # 返回预测属于某标签的概率 print("x_test所属的类别概率:",clf.predict_proba(x_test)) ● 正确答...
')print(clf.predict([[height,weight,style]]))如果用户是1.72米和68公斤,我想展示绿色和紫色的裙子这个例子只返回 浏览7提问于2016-11-23得票数 6 回答已采纳 1回答 python libSVM包装器的'predict‘和'predict_proba’方法不一致 、、 我正在使用libSVM python包装器进行二进制分类器预测,并注意到有时我会...
clf.predict_proba(X) :预测X属于各类的概率 clf.predict_log_proba(X) :相当于 np.log(clf.predict_proba()) clf.apply(X) :返回样本预测节点的索引 clf.score(X,y) :返回准确率,即模型预测值与y不同的个数占比(支持样本权重:clf.score(X,y,sample_weight=sample_weight)) clf.decision_path(np.ar...
doc_class_predicted=clf.predict(x_test)#print(doc_class_predicted)#print(y)print(np.mean(doc_class_predicted ==y_test))#准确率与召回率precision, recall, thresholds =precision_recall_curve(y_test, clf.predict(x_test)) answer= clf.predict_proba(x_test)[:,1] ...
train_predict = clf.predict(x_train) test_predict = clf.predict(x_test) ## 由于逻辑回归模型是概率预测模型(前文介绍的 p = p(y=1|x,\theta)),所有我们可以利用 predict_proba 函数预测其概率 train_predict_proba = clf.predict_proba(x_train) test_predict_proba = clf.predict_proba(x_test)...
oof.loc[valid_users, q-1] = clf.predict_proba(valid_x[FEATURES].astype('float32'))[:,1] 模型本地cv 构建真实正确率df: true = oof.copy() for k in range(18): # GET TRUE LABELS tmp = targets.loc[targets.q == k+1].set_index('session').loc[ALL_USERS] ...
y_proba = clf.predict_proba(X_test[:10])print(X_test[:10])print(clf.predict(X_test[:10])) #取测试集前10个进行预测 importances = pd.DataFrame({'feature':X_train.columns,'importance':np.round(clf.feature_importances_,3)})importances = importances.sort_values('importance',ascending=...
precision, recall, thresholds = precision_recall_curve(y_train, clf.predict(x_train)) answer = clf.predict_proba(x)[:,1] print(classification_report(y, answer, target_names = ['thin', 'fat'])) ''' 将整个测试空间的分类结果用不同颜色区分开''' answer...
y_label_new1_predict_proba=lr_clf.predict_proba(x_fearures_new1)y_label_new2_predict_proba=lr_clf.predict_proba(x_fearures_new2)print('The New point 1 predict Probability of each class:\n',y_label_new1_predict_proba)print('The New point 2 predict Probability of each class:\n',y...