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DOC Update documentation of gradient boosting estimators w/ ranges #22153

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Feb 23, 2022
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DOC Update documentation of gradient boosting estimators w/ ranges #22153

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d5b6383
Update gbc docs to include accepted range of scalar params
genvalen Jan 8, 2022
236c8ea
Update gbr docs to include accepted range of scalar params
genvalen Jan 9, 2022
ad783a6
update docs for warm_start
genvalen Jan 11, 2022
69eb0c2
min_samples_leaf: update upper bound to inf
genvalen Jan 24, 2022
99864f0
Update bounds for min_samples_split, min_samples_leaf, and slight edi…
genvalen Jan 31, 2022
22ea6b1
Update sklearn/ensemble/_gb.py
genvalen Feb 7, 2022
4ffebd2
Update sklearn/ensemble/_gb.py
genvalen Feb 7, 2022
51b53af
Apply suggestions from code review
genvalen Feb 7, 2022
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81 changes: 49 additions & 32 deletions sklearn/ensemble/_gb.py
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Original file line number Diff line number Diff line change
Expand Up @@ -920,18 +920,21 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
learning_rate : float, default=0.1
Learning rate shrinks the contribution of each tree by `learning_rate`.
There is a trade-off between learning_rate and n_estimators.
Values must be in the range `(0.0, inf)`.

n_estimators : int, default=100
The number of boosting stages to perform. Gradient boosting
is fairly robust to over-fitting so a large number usually
results in better performance.
Values must be in the range `[1, inf)`.

subsample : float, default=1.0
The fraction of samples to be used for fitting the individual base
learners. If smaller than 1.0 this results in Stochastic Gradient
Boosting. `subsample` interacts with the parameter `n_estimators`.
Choosing `subsample < 1.0` leads to a reduction of variance
and an increase in bias.
Values must be in the range `(0.0, 1.0]`.

criterion : {'friedman_mse', 'squared_error', 'mse', 'mae'}, \
default='friedman_mse'
Expand All @@ -957,10 +960,9 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node:

- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
- If int, values must be in the range `[2, inf)`.
- If float, values must be in the range `(0.0, 1.0]` and `min_samples_split`
will be `ceil(min_samples_split * n_samples)`.

.. versionchanged:: 0.18
Added float values for fractions.
Expand All @@ -972,10 +974,9 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
right branches. This may have the effect of smoothing the model,
especially in regression.

- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
- If int, values must be in the range `[1, inf)`.
- If float, values must be in the range `(0.0, 1.0]` and `min_samples_leaf`
will be `ceil(min_samples_leaf * n_samples)`.

.. versionchanged:: 0.18
Added float values for fractions.
Expand All @@ -984,16 +985,19 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
Values must be in the range `[0.0, 0.5]`.

max_depth : int, default=3
The maximum depth of the individual regression estimators. The maximum
depth limits the number of nodes in the tree. Tune this parameter
for best performance; the best value depends on the interaction
of the input variables.
Values must be in the range `[1, inf)`.

min_impurity_decrease : float, default=0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.
Values must be in the range `[0.0, inf)`.

The weighted impurity decrease equation is the following::

Expand Down Expand Up @@ -1028,10 +1032,9 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
max_features : {'auto', 'sqrt', 'log2'}, int or float, default=None
The number of features to consider when looking for the best split:

- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
`int(max_features * n_features)` features are considered at each
split.
- If int, values must be in the range `[1, inf)`.
- If float, values must be in the range `(0.0, 1.0]` and the features
considered at each split will be `int(max_features * n_features)`.
- If 'auto', then `max_features=sqrt(n_features)`.
- If 'sqrt', then `max_features=sqrt(n_features)`.
- If 'log2', then `max_features=log2(n_features)`.
Expand All @@ -1048,11 +1051,13 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
Enable verbose output. If 1 then it prints progress and performance
once in a while (the more trees the lower the frequency). If greater
than 1 then it prints progress and performance for every tree.
Values must be in the range `[0, inf)`.

max_leaf_nodes : int, default=None
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
Values must be in the range `[2, inf)`.
If `None`, then unlimited number of leaf nodes.

warm_start : bool, default=False
When set to ``True``, reuse the solution of the previous call to fit
Expand All @@ -1061,7 +1066,7 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):

validation_fraction : float, default=0.1
The proportion of training data to set aside as validation set for
early stopping. Must be between 0 and 1.
early stopping. Values must be in the range `(0.0, 1.0)`.
Only used if ``n_iter_no_change`` is set to an integer.

.. versionadded:: 0.20
Expand All @@ -1074,21 +1079,24 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
data as validation and terminate training when validation score is not
improving in all of the previous ``n_iter_no_change`` numbers of
iterations. The split is stratified.
Values must be in the range `[1, inf)`.

.. versionadded:: 0.20

tol : float, default=1e-4
Tolerance for the early stopping. When the loss is not improving
by at least tol for ``n_iter_no_change`` iterations (if set to a
number), the training stops.
Values must be in the range `(0.0, inf)`.

.. versionadded:: 0.20

ccp_alpha : non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details.
``ccp_alpha`` will be chosen. By default, no pruning is performed.
Values must be in the range `[0.0, inf)`.
See :ref:`minimal_cost_complexity_pruning` for details.

.. versionadded:: 0.22

Expand Down Expand Up @@ -1497,18 +1505,21 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):
learning_rate : float, default=0.1
Learning rate shrinks the contribution of each tree by `learning_rate`.
There is a trade-off between learning_rate and n_estimators.
Values must be in the range `(0.0, inf)`.

n_estimators : int, default=100
The number of boosting stages to perform. Gradient boosting
is fairly robust to over-fitting so a large number usually
results in better performance.
Values must be in the range `[1, inf)`.

subsample : float, default=1.0
The fraction of samples to be used for fitting the individual base
learners. If smaller than 1.0 this results in Stochastic Gradient
Boosting. `subsample` interacts with the parameter `n_estimators`.
Choosing `subsample < 1.0` leads to a reduction of variance
and an increase in bias.
Values must be in the range `(0.0, 1.0]`.

criterion : {'friedman_mse', 'squared_error', 'mse', 'mae'}, \
default='friedman_mse'
Expand All @@ -1533,10 +1544,9 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):
min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node:

- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
- If int, values must be in the range `[2, inf)`.
- If float, values must be in the range `(0.0, 1.0]` and `min_samples_split`
will be `ceil(min_samples_split * n_samples)`.

.. versionchanged:: 0.18
Added float values for fractions.
Expand All @@ -1548,10 +1558,9 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):
right branches. This may have the effect of smoothing the model,
especially in regression.

- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
- If int, values must be in the range `[1, inf)`.
- If float, values must be in the range `(0.0, 1.0]` and `min_samples_leaf`
will be `ceil(min_samples_leaf * n_samples)`.

.. versionchanged:: 0.18
Added float values for fractions.
Expand All @@ -1560,16 +1569,19 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
Values must be in the range `[0.0, 0.5]`.

max_depth : int, default=3
Maximum depth of the individual regression estimators. The maximum
depth limits the number of nodes in the tree. Tune this parameter
for best performance; the best value depends on the interaction
of the input variables.
Values must be in the range `[1, inf)`.

min_impurity_decrease : float, default=0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.
Values must be in the range `[0.0, inf)`.

The weighted impurity decrease equation is the following::

Expand Down Expand Up @@ -1605,10 +1617,9 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):
max_features : {'auto', 'sqrt', 'log2'}, int or float, default=None
The number of features to consider when looking for the best split:

- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
`int(max_features * n_features)` features are considered at each
split.
- If int, values must be in the range `[1, inf)`.
- If float, values must be in the range `(0.0, 1.0]` and the features
considered at each split will be `int(max_features * n_features)`.
- If "auto", then `max_features=n_features`.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
Expand All @@ -1624,16 +1635,19 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):
alpha : float, default=0.9
The alpha-quantile of the huber loss function and the quantile
loss function. Only if ``loss='huber'`` or ``loss='quantile'``.
Values must be in the range `(0.0, 1.0)`.

verbose : int, default=0
Enable verbose output. If 1 then it prints progress and performance
once in a while (the more trees the lower the frequency). If greater
than 1 then it prints progress and performance for every tree.
Values must be in the range `[0, inf)`.

max_leaf_nodes : int, default=None
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
Values must be in the range `[2, inf)`.
If None, then unlimited number of leaf nodes.

warm_start : bool, default=False
When set to ``True``, reuse the solution of the previous call to fit
Expand All @@ -1642,7 +1656,7 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):

validation_fraction : float, default=0.1
The proportion of training data to set aside as validation set for
early stopping. Must be between 0 and 1.
early stopping. Values must be in the range `(0.0, 1.0)`.
Only used if ``n_iter_no_change`` is set to an integer.

.. versionadded:: 0.20
Expand All @@ -1655,21 +1669,24 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):
data as validation and terminate training when validation score is not
improving in all of the previous ``n_iter_no_change`` numbers of
iterations.
Values must be in the range `[1, inf)`.

.. versionadded:: 0.20

tol : float, default=1e-4
Tolerance for the early stopping. When the loss is not improving
by at least tol for ``n_iter_no_change`` iterations (if set to a
number), the training stops.
Values must be in the range `(0.0, inf)`.

.. versionadded:: 0.20

ccp_alpha : non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details.
``ccp_alpha`` will be chosen. By default, no pruning is performed.
Values must be in the range `[0.0, inf)`.
See :ref:`minimal_cost_complexity_pruning` for details.

.. versionadded:: 0.22

Expand Down