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

Code before:
    This estimator has native support for missing values (NaNs) for
    random splits. During training, a random threshold will be chosen
    to split the non-missing values on. Then the non-missing values will be sent
    to the left and right child based on the randomly selected threshold, while
    the missing values will also be randomly sent to the left or right child.
    This is repeated for every feature considered at each split. The best split
    among these is chosen.

    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.

        .. 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.


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:
    random splits. During training, a random threshold will be chosen
    to split the non-missing values on. Then the non-missing values will be sent
    to the left and right child based on the randomly selected threshold, while
    the missing values will also be randomly sent to the left or right child.
    This is repeated for every feature considered at each split. The best split
    among these is chosen.

    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.

        .. 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.