8000 DOC replace deviance by loss in docstring of GradientBoosting by lorentzenchr · Pull Request #25968 · scikit-learn/scikit-learn · GitHub
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DOC replace deviance by loss in docstring of GradientBoosting #25968

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Mar 28, 2023
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DOC replace deviance by loss in docstring of GradientBoosting #25968

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22 changes: 11 additions & 11 deletions sklearn/ensemble/_gb.py
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Original file line number Diff line number Diff line change
Expand Up @@ -622,7 +622,7 @@ def _fit_stages(
X_csr,
)

# track deviance (= loss)
# track loss
if do_oob:
self.train_score_[i] = loss_(
y[sample_mask],
Expand Down Expand Up @@ -1056,28 +1056,28 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
:func:`sklearn.inspection.permutation_importance` as an alternative.

oob_improvement_ : ndarray of shape (n_estimators,)
The improvement in loss (= deviance) on the out-of-bag samples
The improvement in loss on the out-of-bag samples
relative to the previous iteration.
``oob_improvement_[0]`` is the improvement in
loss of the first stage over the ``init`` estimator.
Only available if ``subsample < 1.0``.

oob_scores_ : ndarray of shape (n_estimators,)
The full history of the loss (= deviance) values on the out-of-bag
The full history of the loss values on the out-of-bag
samples. Only available if `subsample < 1.0`.

.. versionadded:: 1.3

oob_score_ : float
The last value of the loss (= deviance) on the out-of-bag samples. It is
The last value of the loss on the out-of-bag samples. It is
the same as `oob_scores_[-1]`. Only available if `subsample < 1.0`.

.. versionadded:: 1.3

train_score_ : ndarray of shape (n_estimators,)
The i-th score ``train_score_[i]`` is the deviance (= loss) of the
The i-th score ``train_score_[i]`` is the loss of the
model at iteration ``i`` on the in-bag sample.
If ``subsample == 1`` this is the deviance on the training data.
If ``subsample == 1`` this is the loss on the training data.

init_ : estimator
The estimator that provides the initial predictions.
Expand Down Expand Up @@ -1619,28 +1619,28 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):
:func:`sklearn.inspection.permutation_importance` as an alternative.

oob_improvement_ : ndarray of shape (n_estimators,)
The improvement in loss (= deviance) on the out-of-bag samples
The improvement in loss on the out-of-bag samples
relative to the previous iteration.
``oob_improvement_[0]`` is the improvement in
loss of the first stage over the ``init`` estimator.
Only available if ``subsample < 1.0``.

oob_scores_ : ndarray of shape (n_estimators,)
The full history of the loss (= deviance) values on the out-of-bag
The full history of the loss values on the out-of-bag
samples. Only available if `subsample < 1.0`.

.. versionadded:: 1.3

oob_score_ : float
The last value of the loss (= deviance) on the out-of-bag samples. It is
The last value of the loss on the out-of-bag samples. It is
the same as `oob_scores_[-1]`. Only available if `subsample < 1.0`.

.. versionadded:: 1.3

train_score_ : ndarray of shape (n_estimators,)
The i-th score ``train_score_[i]`` is the deviance (= loss) of the
The i-th score ``train_score_[i]`` is the loss of the
model at iteration ``i`` on the in-bag sample.
If ``subsample == 1`` this is the deviance on the training data.
If ``subsample == 1`` this is the loss on the training data.

init_ : estimator
The estimator that provides the initial predictions.
Expand Down
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