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' (default), 'micro', 'macro', 'samples', 'weighted']对于多类/多标签...
>>> from sklearn.metrics import precision_score >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> precision_score(y_true, y_pred, average='macro') 0.22... >>> precision_score(y_true, y_pred, average='micro') 0.33... >>> precision_score...
average_precision_score:计算预测值的AP f1_score: 计算F1值,也被称为平衡F-score或F-meature fbeta_score: 计算F-beta score precision_recall_curve:计算不同概率阀值的precision-recall对 precision_recall_fscore_support:为每个类计算precision, recall, F-measure 和 support precision_score: 计算precision r...
1] y_true = [0, 1 ,2, 0 ,1, 2] print(precision_score(y_true, y_pred, average='micro')) # 0.3333333333333333 print(precision_score(y_true, y_pred, average='macro')) # 0.2222222222222222 print(precision_score(y_true, y_pred, average='weighted')) # 0.2222222222222222 print(precision...
首先我们看一下sklearn包中计算precision_score的命令: sklearn.metrics.precision_score(y_true, y_pred, labels=None, pos_label=1, average=’binary’, sample_weight=None) 其中,average参数定义了该指标的计算方法,二分类时average参数默认是binary,多分类时,可选参数有micro、macro、weighted和samples。samples...
首先我们看一下sklearn包中计算precision_score的命令: sklearn.metrics.precision_score(y_true, y_pred, labels=None, pos_label=1, average=’binary’, sample_weight=None) 其中,average参数定义了该指标的计算方法,二分类时average参数默认是binary,多分类时,可选参数有micro、macro、weighted和samples。samples...
(x_test)"""精确率:precision_score()(重)"""print("精确率评估方法的分数:", precision_score(y_test, y_data, average='micro'))"""F1值f1_score()(重)"""print("F1值评估方法的分数:", f1_score(y_test, y_data, average='micro'))# 注意点,如果是二分类的话average不需要更改,如果是多...
precision = PPV = TP/(TP+FP) 在sklearn.metrics.f1_score中存在一个较为复杂的参数是average,其有多个选项——None, ‘binary’ (default), ‘micro’, ‘macro’, ‘samples’, ‘weighted’。下面简单对这些参数进行解释: None, 当选择此参数时,则会输出每一个类别的f1-score; ...
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') # 指定特定分类标签的精确率Out[133]: 0.5 - 其中average参数有五种:(None, ‘micro’, ‘macro’, ‘weighted’, ...