himalaya.scoring.correlation_score_split¶
- himalaya.scoring.correlation_score_split(y_true, y_pred)[source]¶
Split the correlation score into individual components.
When estimating a linear joint model, the predictions of each feature space are summed:
Yhat_joint = Yhat_A + Yhat_B + ... + Yhat_Z
The joint model correlation score r can be computed as:
r_joint = r(Y, Yhat_joint)
This function estimates the contribution of each feature space to the joint model correlation score r such that:
r_joint = r_A + r_B + ... + r_Z
- Parameters
- y_truearray or Tensor of shape (n_samples, n_targets)
Ground truth.
- y_predarray or Tensor of shape (n_predictions, n_samples, n_targets) or (n_samples, n_targets)
Predictions.
- Returns
- correlationsarray of shape (n_predictions, n_targets) or (n_targets, )
Contributions of each individual feature space to the joint correlation score.