利用sklearn计算决定系数R2

决定系数R2

 

sklearn.metrics中r2_score

格式

sklearn.metrics.r2_score(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)

R^2 (coefficient of determination) regression score function.

R2可以是负值（因为模型可以任意差）。如果一个常数模型总是预测y的期望值，而忽略输入特性，则r^2的分数将为0.0。

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 R^2 score of 0.0.

Parameters:

y_true : array-like of shape = (n_samples) or (n_samples, n_outputs) Ground truth (correct) target values. y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs) Estimated target values. sample_weight : array-like of shape = (n_samples), optional Sample weights. multioutput : string in [‘raw_values’, ‘uniform_average’, ‘variance_weighted’] or None or array-like of shape (n_outputs) Defines aggregating of multiple output scores. Array-like value defines weights used to average scores. Default is “uniform_average”. ‘raw_values’ : Returns a full set of scores in case of multioutput input. ‘uniform_average’ : Scores of all outputs are averaged with uniform weight. ‘variance_weighted’ : Scores of all outputs are averaged, weighted by the variances of each individual output.

Returns:	z : float or ndarray of floats The R^2 score or ndarray of scores if ‘multioutput’ is ‘raw_values’.

注意：

This is not a symmetric function. （R2是非对称函数！注意输入顺序。）

Unlike most other scores, R^2 score may be negative (it need not actually be the square of a quantity R).（R2可以是负值，它不需要是R的平方！）

 

from sklearn.metrics import r2_score
 y_true = y_true = [3, -0.5, 2, 7]
 y_pred = [2.5, 0.0, 2, 8]
 r2_score(y_true, y_pred)
 # 结果：0.9486081370449679
 r2_score(y_true, y_pred, multioutput= 'uniform_average')
 # 结果：0.9486081370449679
 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')
 # 结果：0.9382566585956417
 y_true = [1, 2, 3]
 y_pred = [1, 2, 3]
 r2_score(y_true, y_pred)
 # 结果： 1.0
 y_true = [1, 2, 3]
 y_pred = [2, 2, 2]
 r2_score(y_true, y_pred)
 # 结果：0.0
  y_true = [1, 2, 3] # bar{y} = （1+2+3）/ 3 = 2
  y_pred = [3, 2, 1] # y - hat{y}(即y_true - y_pred) = [-2, 0, 2]
  r2_score(y_true, y_pred)
  # 结果：-3.0
  y_true = [[0.5, 1], [-1, 1], [7, -6]]
  y_pred = [[0, 2], [-1, 2], [8, -5]]
  r2_score(y_true, y_pred, multioutput='raw_values')
  # 结果：array([0.96543779, 0.90816327])