Wrapper for https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html :math: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 :math:`R^2` score of 0.0. In the particular case when ``y_true`` is constant, the :math:`R^2` score is not finite: it is either ``NaN`` (perfect predictions) or ``-Inf`` (imperfect predictions). To prevent such non-finite numbers to pollute higher-level experiments such as a grid search cross-validation, by default these cases are replaced with 1.0 (perfect predictions) or 0.0 (imperfect predictions) respectively. You can set ``force_finite`` to ``False`` to prevent this fix from happening. Note: when the prediction residuals have zero mean, the :math:`R^2` score is identical to the `Explained Variance score `. Parameters ---------- solution : DataFrame of shape (n_samples,) or (n_samples, n_outputs) Ground truth (correct) target values. submission : DataFrame of shape (n_samples,) or (n_samples, n_outputs) Estimated target values. weights_column_name: optional str, the name of the sample weights column in the solution file. `