BUG Fixes sample weights when there are missing values in DecisionTrees #26376
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jjerphan
reviewed
May 15, 2023
| @@ -2549,7 +2549,8 @@ def test_missing_values_poisson(): | |||
| (datasets.make_classification, DecisionTreeClassifier), | |||
| ], | |||
| ) | |||
| def test_missing_values_is_resilience(make_data, Tree): | |||
| @pytest.mark.parametrize("sample_weight_train", [None, "ones"]) | |||
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Should we also test the behavior when using non-uniform weights?
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Probably, an easier test than non-uniform weight is to assign 0-weight to some specific samples.
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It might be worth having a separated test for checking an equivalence.
jjerphan
reviewed
May 16, 2023
jjerphan
approved these changes
May 16, 2023
ogrisel
approved these changes
May 16, 2023
| X, y = make_data(n_samples=n_samples, n_features=n_features, random_state=rng) | ||
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| # Create dataset with missing values | ||
| X[rng.choice([False, True], size=X.shape, p=[0.9, 0.1])] = np.nan |
REDVM
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to REDVM/scikit-learn
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Nov 16, 2023
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Reference Issues/PRs
Follow up to #23595
What does this implement/fix? Explain your changes.
On
main, theweighted_n_missingwas incorrectly computed. This PR fixes it. For reference the computation is exactly the same assum_total:scikit-learn/sklearn/tree/_criterion.pyx
Lines 818 to 824 in 6be774b