metrics模块

class sklearn.metrics

方法

1.分类问题的度量

 metrics.accuracy_score
 metrics.auc
 metrics.f1_score
 metrics.precision_score
 metrics.recall_score
 metrics.roc_auc_score
 ......

2.回归问题的度量
3.概率分布函数的度量
4.检索问题的度量
5.其他
查询地址： 

https://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics

　　

基于sklearn上聚类可使用的评估指标：

 metrics.adjusted_mutual_info_score(…[, …])
 metrics.adjusted_rand_score(labels_true, …)
 metrics.calinski_harabasz_score(X, labels)
 metrics.davies_bouldin_score(X, labels)
 metrics.completeness_score(labels_true, …)
 metrics.cluster.contingency_matrix(…[, …])
 metrics.fowlkes_mallows_score(labels_true, …)
 metrics.homogeneity_completeness_v_measure(…)
 metrics.homogeneity_score(labels_true, …)
 metrics.mutual_info_score(labels_true, …)
 metrics.normalized_mutual_info_score(…[, …])
 metrics.silhouette_score(X, labels[, …])
 metrics.silhouette_samples(X, labels[, metric])
 metrics.v_measure_score(labels_true, labels_pred)
#大部分的评估指标都需要labels_true, 一些不需要labels_true指标如下