sklearn中回归器性能评估方法

#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
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
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
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
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