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

Code before:
    Parameters
    ----------
    n_estimators : int, default=100
        The number of trees in the forest.

        .. versionchanged:: 0.22
           The default value of ``n_estimators`` changed from 10 to 100
           in 0.22.

    criterion : {"squared_error", "absolute_error", "poisson"}, default="squared_error"
        The function to measure the quality of a split. Supported criteria
        are "squared_error" for the mean squared error, which is equal to
        variance reduction as feature selection criterion and minimizes the L2
        loss using the mean of each terminal node, "absolute_error" for the mean
        absolute error, which minimizes the L1 loss using the median of each terminal
        node, and "poisson" which uses reduction in Poisson deviance to find splits,
        also using the mean of each terminal node.

        .. versionadded:: 0.18
           Mean Absolute Error (MAE) criterion.

        .. versionadded:: 1.0
           Poisson criterion.

        .. versionchanged:: 1.8
            Criterion `"friedman_mse"` was deprecated.

    max_depth : int, default=None
        The maximum depth of the tree. If None, then nodes are expanded until
        all leaves are pure or until all leaves contain less than
        min_samples_split samples.

    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.

        .. 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, then consider `min_samples_leaf` as the minimum number.

Review comment (suggestion):
```suggestion
        .. versionchanged:: 1.9
            Criterion `"friedman_mse"` was deprecated.
```

Code after:
    Parameters
    ----------
    n_estimators : int, default=100
        The number of trees in the forest.

        .. versionchanged:: 0.22
           The default value of ``n_estimators`` changed from 10 to 100
           in 0.22.

    criterion : {"squared_error", "absolute_error", "poisson"}, default="squared_error"
        The function to measure the quality of a split. Supported criteria
        are "squared_error" for the mean squared error, which is equal to
        variance reduction as feature selection criterion and minimizes the L2
        loss using the mean of each terminal node, "absolute_error" for the mean
        absolute error, which minimizes the L1 loss using the median of each terminal
        node, and "poisson" which uses reduction in Poisson deviance to find splits,
        also using the mean of each terminal node.

        .. versionadded:: 0.18
           Mean Absolute Error (MAE) criterion.

        .. versionadded:: 1.0
           Poisson criterion.

        .. versionchanged:: 1.9
            Criterion `"friedman_mse"` was deprecated.

    max_depth : int, default=None
        The maximum depth of the tree. If None, then nodes are expanded until
        all leaves are pure or until all leaves contain less than
        min_samples_split samples.

    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.

        .. 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, then consider `min_samples_leaf` as the minimum number.