(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.
[R^2(y, hat{y}) = 1 - frac{sum_{i=1}^{n} (y_i - hat{y}_i)^2}{sum_{i=1}^{n} (y_i - ar{y})^2}
]
[ar{y} = frac{1}{n} sum_{i=1}^{n} y_i
]
[sum_{i=1}^{n} (y_i - hat{y}_i)^2 = sum_{i=1}^{n} epsilon_i^2
]
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])