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TST Fix doctest due to GradientBoostingClassifier difference with scipy 1.15 #30583

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Jan 6, 2025
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TST Fix doctest due to GradientBoostingClassifier difference with scipy 1.15 #30583

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859b40e
TST Fix doctest due to floating point difference in numpy
lesteve Jan 4, 2025
ea03721
[azure parallel] [free-threaded] [scipy-dev]
lesteve Jan 4, 2025
c043775
[scipy-dev] [azure parallel] use HistGradientBoostingClassifier which…
lesteve Jan 6, 2025
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22 changes: 11 additions & 11 deletions doc/common_pitfalls.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -160,17 +160,17 @@ much higher than expected accuracy score::

>>> from sklearn.model_selection import train_test_split
>>> from sklearn.feature_selection import SelectKBest
>>> from sklearn.ensemble import GradientBoostingClassifier
>>> from sklearn.ensemble import HistGradientBoostingClassifier
>>> from sklearn.metrics import accuracy_score

>>> # Incorrect preprocessing: the entire data is transformed
>>> X_selected = SelectKBest(k=25).fit_transform(X, y)

>>> X_train, X_test, y_train, y_test = train_test_split(
... X_selected, y, random_state=42)
>>> gbc = GradientBoostingClassifier(random_state=1)
>>> gbc = HistGradientBoostingClassifier(random_state=1)
>>> gbc.fit(X_train, y_train)
GradientBoostingClassifier(random_state=1)
HistGradientBoostingClassifier(random_state=1)

>>> y_pred = gbc.predict(X_test)
>>> accuracy_score(y_test, y_pred)
Expand All @@ -189,14 +189,14 @@ data, close to chance::
>>> select = SelectKBest(k=25)
>>> X_train_selected = select.fit_transform(X_train, y_train)

>>> gbc = GradientBoostingClassifier(random_state=1)
>>> gbc = HistGradientBoostingClassifier(random_state=1)
>>> gbc.fit(X_train_selected, y_train)
GradientBoostingClassifier(random_state=1)
HistGradientBoostingClassifier(random_state=1)

>>> X_test_selected = select.transform(X_test)
>>> y_pred = gbc.predict(X_test_selected)
>>> accuracy_score(y_test, y_pred)
0.46
0.5

Here again, we recommend using a :class:`~sklearn.pipeline.Pipeline` to chain
together the feature selection and model estimators. The pipeline ensures
Expand All @@ -207,15 +207,15 @@ is used only for calculating the accuracy score::
>>> X_train, X_test, y_train, y_test = train_test_split(
... X, y, random_state=42)
>>> pipeline = make_pipeline(SelectKBest(k=25),
... GradientBoostingClassifier(random_state=1))
... HistGradientBoostingClassifier(random_state=1))
>>> pipeline.fit(X_train, y_train)
Pipeline(steps=[('selectkbest', SelectKBest(k=25)),
('gradientboostingclassifier',
GradientBoostingClassifier(random_state=1))])
('histgradientboostingclassifier',
HistGradientBoostingClassifier(random_state=1))])

>>> y_pred = pipeline.predict(X_test)
>>> accuracy_score(y_test, y_pred)
0.46
0.5

The pipeline can also be fed into a cross-validation
function such as :func:`~sklearn.model_selection.cross_val_score`.
Expand All @@ -225,7 +225,7 @@ method is used during fitting and predicting::
>>> from sklearn.model_selection import cross_val_score
>>> scores = cross_val_score(pipeline, X, y)
>>> print(f"Mean accuracy: {scores.mean():.2f}+/-{scores.std():.2f}")
Mean accuracy: 0.46+/-0.07
Mean accuracy: 0.43+/-0.05


.. _randomness:
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