可以使用 zero_division 修改此行为。 例子: >>> from sklearn.metrics import recall_score >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> recall_score(y_true, y_pred, average='macro') 0.33... >>> recall_score(y_true, y_pred, average='...
='macro', zero_division=0) recall_macro = recall_score(y_true, y_pred, average='macro', zero_division=0) # 微平均 precision_micro = precision_score(y_true, y_pred, average='micro', zero_division=0) recall_micro = recall_score(y_true, y_pred, average='micro', zero_division=0)...
precision_recall_fscore_support函数用于计算分类任务的精确率(precision)、召回率(recall)、F1分数(F1 score)和支持度(support)。其参数如下: y_true: 真实标签数组。 y_pred: 预测标签数组。 average: 指定计算平均分数的方式。可选值包括'binary'(仅用于二分类)、'micro'(全局计数)、'macro'(平均每个类别的...
0,0,1,0]) print(precision_recall_fscore_support(y_true_1, y_pred_1, average='macro'))...
” k折交叉验证 K折交叉验证(k-fold cross-validation)首先将所有数据分割成K个子样本，不重复的选取...
Fig. 3. Average F1-score comparison between NBEM and other classification models. As presented in Fig. 3, the NBEM method (i.e., the bold blue line) scored a higher F1-score than all of the compared datasets. Since we used β=1, which is used when FN and FP have equal importance...
本文简要介绍python语言中sklearn.metrics.precision_recall_fscore_support的用法。 用法: sklearn.metrics.precision_recall_fscore_support(y_true, y_pred, *, beta=1.0, labels=None, pos_label=1, average=None, warn_for=('precision','recall','f-score'), sample_weight=None, zero_division='warn...
y_pred_1 = np.array([0,0,0,1,0]) print(precision_recall_fscore_support(y_true_1, y_pred_1, average='macro')) 由此,开始了一番对 Precision, recall, and F-score 的探索之路。经过查阅,原来计算 F1-score 有不同的方法: 请看我下面的手稿: ...
sklearn.metrics.fbeta_score(y_true, y_pred, *, beta, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn') Parameters: y_true1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred1d array-like, or label...