Hướng dẫn dùng r2_score python
\(R^2\) (coefficient of determination) regression score function. Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). In the general case when the true y is non-constant, a constant model that always predicts the average y disregarding the input features would get a \(R^2\) score of 0.0. In the particular case when Note: when the prediction residuals have zero mean, the \(R^2\) score is identical to the
Read more in the User Guide. Parameters:y_truearray-like of shape (n_samples,) or (n_samples, n_outputs)Ground truth (correct) target values. Estimated target values. sample_weightarray-like of shape (n_samples,), default=NoneSample weights. multioutput{‘raw_values’, ‘uniform_average’, ‘variance_weighted’}, array-like of shape (n_outputs,) or None, default=’uniform_average’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. Changed in version 0.19: Default value of multioutput is ‘uniform_average’. force_finitebool, default=TrueFlag indicating if New in version 1.1. Returns:zfloat or ndarray of floatsThe \(R^2\) score or ndarray of scores if ‘multioutput’ is ‘raw_values’. Notes This is not a symmetric function. Unlike most other scores, \(R^2\) score may be negative (it need not actually be the square of a quantity R). This metric is not well-defined for single samples and will return a NaN value if n_samples is less than two. References Examples >>> 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) 0.948... >>> 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') 0.938... >>> y_true = [1, 2, 3] >>> y_pred = [1, 2, 3] >>> r2_score(y_true, y_pred) 1.0 >>> y_true = [1, 2, 3] >>> y_pred = [2, 2, 2] >>> r2_score(y_true, y_pred) 0.0 >>> y_true = [1, 2, 3] >>> y_pred = [3, 2, 1] >>> r2_score(y_true, y_pred) -3.0 >>> y_true = [-2, -2, -2] >>> y_pred = [-2, -2, -2] >>> r2_score(y_true, y_pred) 1.0 >>> r2_score(y_true, y_pred, force_finite=False) nan >>> y_true = [-2, -2, -2] >>> y_pred = [-2, -2, -2 + 1e-8] >>> r2_score(y_true, y_pred) 0.0 >>> r2_score(y_true, y_pred, force_finite=False) -inf Examples using |