[ENH] Bootstrap for ALE confidence intervals#703
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Thx ! I think that we need to finalize before going to that one.
| X, | ||
| feature_idx, | ||
| grid_resolution="auto", | ||
| percentiles=(0.05, 0.95), |
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| percentiles=(0.05, 0.95), | |
| percentiles=(5, 95), |
| [value_idx[mask_low], value_idx[mask_high] + 1] | ||
| ) | ||
| combined_effects = np.concatenate([local_effects_low, local_effects_high]) | ||
| def _compute_ale_curve(X_sample): |
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I thought that you would have to compute the curve on fixed bins, so that the stdv makes sense ?
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It's what is done by calculating the unique_values outside of the function which compute the curve for a given random draw with replacement from the dataset
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Related to PR #701
Instead of calculating the standard deviation for each bin edge based on a single ALE curve, we calculate multiple curves to obtain more reliable confidence intervals.