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=...
# 需要导入模块: from sklearn import metrics [as 别名]# 或者: from sklearn.metrics importprecision_recall_fscore_support[as 别名]deftest_precision_recall_f1_no_labels(beta, average):y_true = np.zeros((20,3)) y_pred = np.zeros_like(y_true) p, r, f, s = assert_warns(UndefinedMetr...
There is a chance that some class are missing at each fold. So you would sometimes average over a different number of labels. How do you decide which label must be considered as positive without assuming a label ordering? These two issues can be handled by specifying a special scorer with ...
shape[1] precision, recall, f_value, support = precision_recall_fscore_support(ground_truth, prediction_indices, beta=f_beta, pos_label=M, average=avg_method) else: precision, recall, f_value, support = precision_recall_fscore_support(ground_truth, prediction_indices, beta=f_beta, average...
precision, recall, f_value, support \ =precision_recall_fscore_support(ground_truth, prediction_indices, beta=f_beta, average=avg_method)returnprecision, recall, f_value 开发者ID:corsy,项目名称:evaluators,代码行数:31,代码来源:precision_recall_evaluator.py ...