Repository: scikit-learn/scikit-learn
File: sklearn/ensemble/_gb.py (Python)

Code before:

        For an example of the effects of this parameter and its interaction with
        ``subsample``, see
        :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py`.

    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'}, default='friedman_mse'
        This parameter has no effect.

        .. versionadded:: 0.18

        .. deprecated:: 1.8
           `criterion` is deprecated and will be removed in 1.10.

    min_samples_split : int or float, default=2
        The minimum number of samples required to split an internal node:

        - 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.

    min_samples_leaf : int or float, default=1
        The minimum number of samples required to be at a leaf node.
        A split point at any depth will only be considered if it leaves at
        least ``min_samples_leaf`` training samples in each of the left and
        right branches.  This may have the effect of smoothing the model,
        especially in regression.

        - 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.


Review comment (suggestion):
```suggestion
        .. deprecated:: 1.9
           `criterion` is deprecated and will be removed in 1.11.
```

Code after:

        For an example of the effects of this parameter and its interaction with
        ``subsample``, see
        :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py`.

    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'}, default='friedman_mse'
        This parameter has no effect.

        .. versionadded:: 0.18

        .. deprecated:: 1.9
           `criterion` is deprecated and will be removed in 1.11.

    min_samples_split : int or float, default=2
        The minimum number of samples required to split an internal node:

        - 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.

    min_samples_leaf : int or float, default=1
        The minimum number of samples required to be at a leaf node.
        A split point at any depth will only be considered if it leaves at
        least ``min_samples_leaf`` training samples in each of the left and
        right branches.  This may have the effect of smoothing the model,
        especially in regression.

        - 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.