利用sklearn计算决定系数R2

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])