本文简要介绍python语言中 sklearn.metrics.average_precision_score 的用法。 用法: sklearn.metrics.average_precision_score(y_true, y_score, *, average='macro', pos_label=1, sample_weight=None) 根据预测分数计算平均精度 (AP)。 AP 将precision-recall 曲线总结为在每个阈值处实现的精度的加权平均值,...
例子: >>>importnumpyasnp>>>fromsklearn.metricsimportlabel_ranking_average_precision_score>>>y_true = np.array([[1,0,0], [0,0,1]])>>>y_score = np.array([[0.75,0.5,1], [1,0.2,0.1]])>>>label_ranking_average_precision_score(y_true, y_score)0.416......
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 average_precision_score average_precision = average_precision_score(y_test, y_test_proba) precision, recall, thresholds = precision_recall_curve(y_test, y_test_proba) plt.plot(recall, precision, marker=’.’, label=’Logistic’) plt.xlabel(‘Recall’) plt.ylabel(...
average_precision_score(y_true=y_true, y_score=y_pred) 1. 2. 3. 4. 1.3 对数损失(Log-loss) 针对分类输出不是类别,而是类别的概率,使用对数损失函数进行评价。这也是逻辑回归的分类函数,下面是二分类的损失函数。 Log_loss=−1N∑i=1Nyilogpi+(1−yi)log(1−pi) ...
precision_score(y_true, y_pred[, labels, …]) recall_score(y_true, y_pred[, labels, …]) zero_one_loss(y_true, y_pred[, normalize, …]) 还有一些可以同时用于二标签和多标签(不是多分类)问题: average_precision_score(y_true, y_score[, …]) ...
Macro F1:将n分类的评价拆成n个二分类的评价,计算每个二分类的F1 score,n个F1 score的平均值即为Macro F1。 微平均 Micro-average Micro F1:将n分类的评价拆成n个二分类的评价,将n个二分类评价的TP、FP、TN、FN对应相加,计算评价准确率和召回率,由这2个准确率和召回率计算的F1 score即为Micro F1。
1.precision也常称为查准率,recall称为查全率 2.比较常用的是f1, python3.6代码实现: #调用sklearn库中的指标求解from sklearn import metricsfrom sklearn.metrics import precision_recall_curvefrom sklearn.metrics import average_precision_scorefrom sklearn.metrics import accuracy_score#给出分类结果y_pred = ...
f1_score of random forest: 0.610 f1_score of svc: 0.656 fromsklearn.metricsimportaverage_precision_scoreap_rf=average_precision_score(y_test,rf.predict_proba(X_test)[:,1])ap_svc=average_precision_score(y_test,svc.decision_function(X_test))print("Average precision of random forest:{:.3f}...
最后可以根据具体的应用,在曲线上找到最优的点,得到相对应的precision,recall,f1 score等指标,去调整模型的阈值,从而得到一个符合具体应用的模型。 13、非极大值抑制(NMS) Non-Maximum Suppression就是需要根据score矩阵和region的坐标信息,从中找到置信度比较高的bounding box。对于有重叠在一起的预测框,只保留得分最...