>>>labels_pred=[1,1,0,0,3,3]>>>metrics.adjusted_mutual_info_score(labels_true,labels_pred)0.22504... 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_...
p-valuesof feature scores,Noneif`score_func` returnedonly scores. Notes --- Tiesbetween featureswith equal scores will be brokenin an unspecified way. See also --- f_classif: ANOVA F-valuebetween label/featurefor classification tasks. mutual_info_classif: Mutual informationfor a discrete target...
print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels)) 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" % ...
>>>labels_pred = labels_true[:]>>>metrics.adjusted_mutual_info_score(labels_true, labels_pred)1.0>>>metrics.normalized_mutual_info_score(labels_true, labels_pred)1.0 这对于mutual_info_score是不成立的,因此该得分更难于判断: >>>metrics.mutual_info_score(labels_true, labels_pred)0.69... 坏...
from sklearn.pipeline import Pipelinepipe = Pipeline([("scaler",MinMaxScaler()),("svm",SVC())])pip.fit(X_train,y_train)pip.score(X_test,y_test) 2、make_pipeline函数创建管道 用Pipeline类构建管道时语法有点麻烦,我们通常不需要为每一个步骤提供用户指定的名称,这种情况下,就可以用make_pipeline函...
pip.score(X_test,y_test) 1. 2. 3. 4. 2、make_pipeline函数创建管道 用Pipeline类构建管道时语法有点麻烦,我们通常不需要为每一个步骤提供用户指定的名称,这种情况下,就可以用make_pipeline函数创建管道,它可以为我们创建管道并根据每个步骤所属的类为其自动命名。
mutual_info_regression作为参数,用于回归模型。基于互信息选择特征。互信息用于度量 X 和 Y 共享的信息:度量知道这两个变量其中一个,对另一个不确定度减少的程度。 由于score_func与SelectKBest实现框架是分离的,可以将输入的sscore_func替换成其他计算函数或lambda表达式,用于实现自定义单变量特征选择的方法,例如基于...
一、使用sklearn数据挖掘 大数据分析数据挖掘工具sklearn使用指南 1、数据挖掘的步骤 数据挖掘通常包括数据...
2.3.10.2. Mutual Information based scores from sklearnimportmetricslabels_true=[0,0,0,1,1,1] labels_pred = [0,0,1,1,2,2] metrics.adjusted_mutual_info_score(labels_true, labels_pred) normalized_mutual_info_score metrics.normalized_mutual_info_score(labels_true, labels_pred) ...
(x_train)# 预测训练集x_train中的分类结果,并与训练集y_train比对以得出训练集准确率acc_train=accuracy_score(pred_train,y_train)pred_test=dtc.predict(x_test)# 预测测试集x_test中的分类结果,并与测试集y_test比对以得出测试集准确率acc_test=accuracy_score(pred_test,y_test)print(acc_train,acc_...