Add score_ attribute based on pooled local model performance (BaseClassifier) - #118
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…ysal#117) Bumps the github-actions group with 1 update: [actions/github-script](https://github.com/actions/github-script). Updates `actions/github-script` from 8 to 9 - [Release notes](https://github.com/actions/github-script/releases) - [Commits](actions/github-script@v8...v9) --- updated-dependencies: - dependency-name: actions/github-script dependency-version: '9' dependency-type: direct:production update-type: version-update:semver-major dependency-group: github-actions ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
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This is a duplicate of #76. Please see the discussion there. |
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This PR introduces a score_ attribute to BaseClassifier, computed after fit() for convenient access to training performance.
Motivation:
There has been some ambiguity around how model performance is evaluated, as discussed in Issue #30 (API consistency for metrics). Currently, .score() relies on predict(), which uses an ensemble of local models. However, during training we already compute local model outputs that can be aggregated more directly.
This PR follows the direction outlined in the discussion and TODO:
TODO: score_ should be an alias of pooled_score_(linear_model.py)
Implementation:
Added computation of pooled_score_ using:
concatenated _y_local
concatenated _pred_local
Defined:
self.score_ = self.pooled_score_
This ensures:
score_ reflects pooled local model performance
avoids confusion with .score() (ensemble-based)
(Not reused .score() which uses predict() which ultimately uses ensemble of local models instead used score_ which uses pooled local model outputs)
Tests:
score_attribute exists
score_matches pooled_score_
value is within valid range
(Applies only to BaseClassifier for now and does not modify existing .score() behavior)
Open to feedbacks and suggestions.