(y_true, y_pred, average='macro') (0.22..., 0.33..., 0.26..., None) >>> precision_recall_fscore_support(y_true, y_pred, average='micro') (0.33..., 0.33..., 0.33..., None) >>> precision_recall_fscore_support(y_true, y_pred, average='weighted') (0.22..., 0.33...,...
# 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 ...
(y_true, y_pred, beta=2, average=None) support = s assert_array_almost_equal(f2, [0, 0.55, 1, 0], 2) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="macro") assert_almost_equal(p, 0.5) assert_almost_equal(r, 1.5 / 4) assert_almost_equal(f, ...
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 示例3: learnCART ▲点赞 4▼ deflearnCART(self):train_input_data = self.loadData(self.train_file) ...