标签：learn mat ssi div eps sre spl average reg
\(R^2\)不止一种定义方式，这里是scikit-learn中所使用的定义。
As such variance is dataset dependent, R2 may not be meaningfully comparable across different datasets. Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R2 score of 0.0.
As such variance is dataset dependent, R2 may not be meaningfully comparable across different datasets. Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R2 score of 0.0.
from sklearn.metrics import r2_score
y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
r2_score(y_true, y_pred)
y_true = [[0.5, 1], [-1, 1], [7, -6]]
y_pred = [[0, 2], [-1, 2], [8, -5]]
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]]
r2_score(y_true, y_pred, multioutput=‘uniform_average‘)
r2_score(y_true, y_pred, multioutput=‘raw_values‘)
r2_score(y_true, y_pred, multioutput=[0.3, 0.7])
标签：learn mat ssi div eps sre spl average reg
原文地址：https://www.cnblogs.com/yaos/p/14016352.html
