print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels)) print("Adjusted Rand Index: %0.3f" % metrics.adjusted_rand_score(labels_true, labels)) print("Adjusted Mutual Information: %0.3f" % metrics.adjusted_mutual_info_score(labels_true, labels)) print("Silhouette Coeffici...
全部的,mutual_info_score,adjusted_mutual_info_score和normalized_mutual_info_score是 symmetric(对称的): 交换参数不会更改分数。因此,它们可以用作consensus measure: >>>metrics.adjusted_mutual_info_score(labels_pred,labels_true)0.22504... 完美标签得分是 1.0: >>>labels_pred=labels_true[:]>>>metrics...
对于n_clusters和n_samples的任何值(这不是未经过调整的 互信息(Mutual Information)或者V-measure的情况)。 上界为1:得分值接近于0表明两个标签分配集合很大程度上是独立的,而得分值接近于1表明两个标签分配集合具有很大的一致性。更进一步,正好是1的AMI表示两个标签分配相等。(带有或不带有排列)。
2.3.9.2. 基于 Mutual Information (互信息)的分数 2.3.9.2.1. 优点 2.3.9.2.2. 缺点 2.3.9.2.3. 数学公式 2.3.9.3. 同质性,完整性和 V-measure 2.3.9.3.1. 优点 2.3.9.3.2. 缺点 2.3.9.3.3. 数学表达 2.3.9.4. Fowlkes-Mallows 分数 2.3.9.4.1. 优点 2.3.9.4.2. 缺点 2.3.9.5. Silhouette ...
'normalized_mutual_info_score’ metrics.normalized_mutual_info_score 'v_measure_score’ metrics.v_measure_score Regression 'explained_variance’ metrics.explained_variance_score 'max_error’ metrics.max_error 'neg_mean_absolute_error’ metrics.mean_absolute_error ...
为了简化构建变换和模型链的过程,Scikit-Learn提供了pipeline类,可以将多个处理步骤合并为单个Scikit-Learn估计器。pipeline类本身具有fit、predict和score方法,其行为与Scikit-Learn中的其他模型相同。 sklearn的make_pipeline函数的代码解释 sklearn的make_pipeline函数的使用方法 ...
mutual_info_score metrics.mutual_info_score(labels_true, labels_pred) 2.3.10.3. Homogeneity, completeness and V-measure from sklearnimportmetricslabels_true=[0,0,0,1,1,1] labels_pred = [0,0,1,1,2,2] metrics.homogeneity_score(labels_true, labels_pred) ...
1.2 Mutual Information based scores 互信息 Two different normalized versions of this measure are available, Normalized Mutual Information(NMI) and Adjusted Mutual Information(AMI). NMI is often used in the literature while AMI was proposed more recently and is normalized against chance: >>>from sk...
‘normalized_mutual_info_score’ metrics.normalized_mutual_info_score ‘v_measure_score’ metrics.v_measure_score Regression ‘explained_variance’ metrics.explained_variance_score ‘max_error’ metrics.max_error ‘neg_mean_absolute_error’ metrics.mean_absolute_error ...
def calculate_scores(self): x, c, labels = self.x, self.c, self.labels self.v_measure = v_measure_score(c, labels) self.complete = completeness_score(c, labels) self.adjusted_mutual = adjusted_mutual_info_score(c, labels) self.adjusted_rand = adjusted_rand_score(c...