scikit-learn 库实现的 f1_score 函数计算 F1 分数,一个常用代码示例如下所示: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 import numpy as np from sklearn.metrics import f1_score # Ground truth (true labels) y_true = np.array([0, 1, 2, 0, 1, 2, 0, 1, 2]) # Predicted labe...
File [~\Languages\Lib\site-packages\sklearn\metrics\_classification.py:1789](http://localhost:8888/lab/tree/rainfall_aus/~/Languages/Lib/site-packages/sklearn/metrics/_classification.py#line=1788), in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_fo...
To assist the evaluation of classification results, the package provides a score functionscore_eval_func()with multiple metrics. One can usemake_scorer()method in sk-learn andfunctoolsto specify the evaluation score. The method will be compatible with sk-learn cross validation and model selection ...
In addition, more evaluation indicators for imbalanced learning need to be taken into account, such as precision, F1-score, etc. 5. Conclusions In this study, by applying the LR model, the GBDT model, and the weighted GBDT model to the landslide susceptibility mapping, we can get the ...
y_score:预测得分,可以是正类的估计概率、置信值或者分类器方法的返回值 y_true:每个样本的真实类别,必须为0(反例),1(正例)标记 from sklearn.metrics import roc_auc_score # 0.5~1之间,越接近于1约好 y_test = np.where(y_test > 2.5, 1, 0) print("AUC指标:",) roc_auc_score(y_true=y_te...