利用sklearn计算决定系数R2

决定系数R2

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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])

 

转载于:https://www.cnblogs.com/jiangkejie/p/10677858.html

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