- explained_variance_score()
- mean_absolute_error()
- mean_squared_error()
- r2_score()
以上四个函数的相同点：
- 这些函数都有一个参数“multioutput”，用来指定在多目标回归问题中，若干单个目标变量的损失或得分以什么样的方式被平均起来
- 它的默认值是“uniform_average”，他就是将所有预测目标值的损失以等权重的方式平均起来
- 如果你传入了一个shape为（n_oupputs,）的ndarray，那么数组内的数将被视为是对每个输出预测损失（或得分）的加权值，所以最终的损失就是按照你锁指定的加权方式来计算的
- 如果multioutput是“raw_values”,那么所有的回归目标的预测损失或预测得分都会被单独返回一个shape是（n_output）的数组中
explained_variance_score
#explained_variance_score from sklearn.metrics import explained_variance_score y_true=[3,-0.5,2,7] y_pred=[2.5,0.0,2,8] print(explained_variance_score(y_true,y_pred)) y_true=[[0.5,1],[-1,1],[7,-6]] y_pred=[[0,2],[-1,2],[8,-5]] print(explained_variance_score(y_true,y_pred,multioutput="raw_values")) print(explained_variance_score(y_true,y_pred,multioutput=[0.3,0.7])) #结果
#0.957173447537 #[ 0.96774194 1. ] #0.990322580645
mean_absolute_error
#mean_absolute_error from sklearn.metrics import mean_absolute_error y_true=[3,0.5,2,7] y_pred=[2.5,0.0,2,8] print(mean_absolute_error(y_true,y_pred)) y_true=[[0.5,1],[-1,1],[7,-6]] y_pred=[[0,2],[-1,2],[8,-5]] print(mean_absolute_error(y_true,y_pred)) print(mean_absolute_error(y_true,y_pred,multioutput="raw_values")) print(mean_absolute_error(y_true,y_pred,multioutput=[0.3,0.7])) #结果 #0.5 #0.75 #[ 0.5 1. ] #0.85
mean_squared_error
#mean_squared_error from sklearn.metrics import mean_squared_error y_true=[3,-0.5,2,7] y_pred=[2.5,0.0,2,8] print(mean_squared_error(y_true,y_pred)) y_true=[[0.5,1],[-1,1],[7,-6]] y_pred=[[0,2],[-1,2],[8,-5]] print(mean_squared_error(y_true,y_pred)) #结果 #0.375 #0.708333333333
median_absolute_error
#median_absolute_error from sklearn.metrics import median_absolute_error y_true=[3,-0.5,2,7] y_pred=[2.5,0.0,2,8] print(median_absolute_error(y_true,y_pred)) #结果 #0.5
r2_score
#r2_score from sklearn.metrics import r2_score y_true=[3,-0.5,2,7] y_pred=[2.5,0.0,2,8] print(r2_score(y_true,y_pred)) y_true=[[0.5,1],[-1,1],[7,-6]] y_pred=[[0,2],[-1,2],[8,-5]] print(r2_score(y_true,y_pred,multioutput="variance_weighted")) y_true=[[0.5,1],[-1,1],[7,-6]] y_pred=[[0,2],[-1,2],[8,-5]] print(r2_score(y_true,y_pred,multioutput="uniform_average")) print(r2_score(y_true,y_pred,multioutput="raw_values")) print(r2_score(y_true,y_pred,multioutput=[0.3,0.7])) #结果 #0.948608137045 #0.938256658596 #0.936800526662 #[ 0.96543779 0.90816327] #0.92534562212