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

Code before:
    should go to the left or right child, based on the potential gain. When predicting,
    samples with missing values are assigned to the left or right child consequently.
    If no missing values were encountered for a given feature during training, then
    samples with missing values are mapped to whichever child has the most samples.

    For a comparison between tree-based ensemble models see the example
    :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.

    Read more in the :ref:`User Guide <forest>`.

    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.
        Training using "absolute_error" is significantly slower
        than when using "squared_error".

        .. 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.


Review comment (suggestion):
```suggestion
        node, and "poisson" which uses reduction in Poisson deviance to find splits, also using the mean of each terminal node.
```

Code after:
    should go to the left or right child, based on the potential gain. When predicting,
    samples with missing values are assigned to the left or right child consequently.
    If no missing values were encountered for a given feature during training, then
    samples with missing values are mapped to whichever child has the most samples.

    For a comparison between tree-based ensemble models see the example
    :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.

    Read more in the :ref:`User Guide <forest>`.

    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