【Python学习】 - sklearn学习 - 评估指标precision_score的参数说明 函数声明:precision_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None)其中较为常用的参数解释如下:y_true:真实标签y_pred:预测标签average:评价值的平均值的计算方式。可以接收[None, 'binary' (def...
def get_score(self): return self._score def set_score(self, value): if not isinstance(value, int): raise ValueError('score must be an integer!') if value < 0 or value > 100: raise ValueError('score must between 0 ~ 100!') self._score = value 1. 2. 3. 4. 5. 6. 7. 8....
metrics.precision_score(y_true, y_pred, average='micro') # 微平均,精确率 Out[130]: 0.33333333333333331 metrics.precision_score(y_true, y_pred, average='macro') # 宏平均,精确率 Out[131]: 0.375 metrics.precision_score(y_true, y_pred, labels=[0, 1, 2, 3], average='macro') # 指定...
accuracy, precision, recall, f1 = evaluate_model(logreg, X_test, y_test) print(f"Logistic Regression: \nAccuracy={accuracy}, \nPrecision={precision}, \nRecall={recall}, \nF1-score={f1}") accuracy, precision, recall, f1 = evaluate_model(rf, X_test, y_test) print(f"\nRandom Forest...
1、accuracy_score 与 precision_score accuracy_score准确率,顾名思义就是分类结果中正确分类的数据比总数目(不论是两个还是多类); precision_score 这个有时人们也称为其准确率,但是它有另外一个名称查准率,这个就是有正例和负例的区别了(一般来说正例就是我们所关注的那个类别), 这个的准确定义为: ...
returns:dtype:float'''TP,FN,FP,TN=prep_clf(obs=obs,pre=pre,threshold=threshold)return(TP+TN)/(TP+TN+FP+FN)defFSC(obs,pre,threshold=0.1):'''func:计算f1 score=2*((precision*recall)/(precision+recall))''' precision_socre=precision(obs,pre,threshold=threshold)recall_score=recall(obs,pre...
recall = recall_score(y_test, y_pred) f1 = f1_score(y_test, y_pred) print(f"准确率:{accuracy}") print(f"精确度:{precision}") print(f"召回率:{recall}") print(f"F1分数:{f1}") 在上面的示例中,我们首先加载了Iris数据集,并将其转化为二元分类问题。然后,我们使用Logistic回归模型进行训练...
python中想要计算如上指标主要是使用sklearn包中预先写好的函数。可以使用以下代码进行计算: fromsklearn.metricsimportprecision_score, recall_score, f1_score, accuracy_scorey_true = [...]# 正确的标签y_pred = [...]# 预测的标签# 计算正确率accuracy = accuracy_score(y_true, y_pred)# 计算精确度...
编写python 代码,完成 precision_score 函数和 recall_score 函数分别实现计算精准率和召回率。 precision_score函数中的参数: y_true :数据的真实类别,类型为 ndarray; y_predict :模型预测的类别,类型为 ndarray。 recall_score 函数中的参数: y_true:数据的..
accuracy=accuracy_score(y_test,y_pred)precision=precision_score(y_test,y_pred)recall=recall_score(y_test,y_pred)f1=f1_score(y_test,y_pred)print(f"准确率:{accuracy}")print(f"精确度:{precision}")print(f"召回率:{recall}")print(f"F1分数:{f1}") ...