.. _`Random Forest Classifier`:

.. _`org.sysess.sympathy.machinelearning.random_forest_classifier`:

Random Forest Classifier
~~~~~~~~~~~~~~~~~~~~~~~~

.. image:: forest.svg
   :width: 48


A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap is True (default).

*Configuration*:


  - *n_estimators*

    The number of trees in the forest.

    .. versionchanged:: 0.20
       The default value of ``n_estimators`` will change from 10 in
       version 0.20 to 100 in version 0.22.


  - *criterion*

    The function to measure the quality of a split. Supported criteria are
    "gini" for the Gini impurity and "entropy" for the information gain.
    Note: this parameter is tree-specific.


  - *bootstrap*

    Whether bootstrap samples are used when building trees.


  - *oob_score*

    Whether to use out-of-bag samples to estimate
    the generalization accuracy.


  - *n_jobs*

    The number of jobs to run in parallel for both `fit` and `predict`.
    ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
    ``-1`` means using all processors. See n_jobs
    for more details.


  - *max_features*

    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 "auto", then `max_features=sqrt(n_features)`.
    - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
    - If "log2", then `max_features=log2(n_features)`.
    - If None, then `max_features=n_features`.

    Note: the search for a split does not stop until at least one
    valid partition of the node samples is found, even if it requires to
    effectively inspect more than ``max_features`` features.


  - *max_depth*

    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*

    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*

    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.
    - 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.

    .. versionchanged:: 0.18
       Added float values for fractions.


  - *min_samples_leaf*

    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.
    - 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.

    .. versionchanged:: 0.18
       Added float values for fractions.


  - *min_weight_fraction_leaf*

    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.


  - *max_leaf_nodes*

    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.


  - *min_impurity_split*

    Threshold for early stopping in tree growth. A node will split
    if its impurity is above the threshold, otherwise it is a leaf.

    .. deprecated:: 0.19
       ``min_impurity_split`` has been deprecated in favor of
       ``min_impurity_decrease`` in 0.19. The default value of
       ``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it
       will be removed in 0.25. Use ``min_impurity_decrease`` instead.



  - *min_impurity_decrease*

    A node will be split if this split induces a decrease of the impurity
    greater than or equal to this value.

    The weighted impurity decrease equation is the following::

        N_t / N * (impurity - N_t_R / N_t * right_impurity
                            - N_t_L / N_t * left_impurity)

    where ``N`` is the total number of samples, ``N_t`` is the number of
    samples at the current node, ``N_t_L`` is the number of samples in the
    left child, and ``N_t_R`` is the number of samples in the right child.

    ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
    if ``sample_weight`` is passed.

    .. versionadded:: 0.19


  - *random_state*

    If int, random_state is the seed used by the random number generator;
    If RandomState instance, random_state is the random number generator;
    If None, the random number generator is the RandomState instance used
    by `np.random`.


  - *warm_start*

    When set to ``True``, reuse the solution of the previous call to fit
    and add more estimators to the ensemble, otherwise, just fit a whole
    new forest. See warm_start.




*Attributes*:


  - *classes_*

    The classes labels (single output problem), or a list of arrays of
    class labels (multi-output problem).


  - *feature_importances_*

    The feature importances (the higher, the more important the feature).


  - *n_classes_*

    The number of classes (single output problem), or a list containing the
    number of classes for each output (multi-output problem).


  - *n_features_*

    The number of features when ``fit`` is performed.


  - *n_outputs_*

    The number of outputs when ``fit`` is performed.


  - *oob_score_*

    Score of the training dataset obtained using an out-of-bag estimate.


  - *oob_decision_function_*

    Decision function computed with out-of-bag estimate on the training
    set. If n_estimators is small it might be possible that a data point
    was never left out during the bootstrap. In this case,
    `oob_decision_function_` might contain NaN.




*Input ports*:


*Output ports*:
    **model** : model
        Model



**n_estimators** (n_estimators)
    The number of trees in the forest.

    .. versionchanged:: 0.20
       The default value of ``n_estimators`` will change from 10 in
       version 0.20 to 100 in version 0.22.
**criterion** (criterion)
    The function to measure the quality of a split. Supported criteria are
    "gini" for the Gini impurity and "entropy" for the information gain.
    Note: this parameter is tree-specific.
**bootstrap** (bootstrap)
    Whether bootstrap samples are used when building trees.
**oob_score** (oob_score)
    Whether to use out-of-bag samples to estimate
    the generalization accuracy.
**n_jobs** (n_jobs)
    The number of jobs to run in parallel for both `fit` and `predict`.
    ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
    ``-1`` means using all processors. See n_jobs
    for more details.
**max_features** (max_features)
    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 "auto", then `max_features=sqrt(n_features)`.
    - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
    - If "log2", then `max_features=log2(n_features)`.
    - If None, then `max_features=n_features`.

    Note: the search for a split does not stop until at least one
    valid partition of the node samples is found, even if it requires to
    effectively inspect more than ``max_features`` features.
**max_depth** (max_depth)
    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** (min_samples_split)
    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** (min_samples_leaf)
    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.
    - 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.

    .. versionchanged:: 0.18
       Added float values for fractions.
**min_weight_fraction_leaf** (min_weight_fraction_leaf)
    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.
**max_leaf_nodes** (max_leaf_nodes)
    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.
**min_impurity_split** (min_impurity_split)
    Threshold for early stopping in tree growth. A node will split
    if its impurity is above the threshold, otherwise it is a leaf.

    .. deprecated:: 0.19
       ``min_impurity_split`` has been deprecated in favor of
       ``min_impurity_decrease`` in 0.19. The default value of
       ``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it
       will be removed in 0.25. Use ``min_impurity_decrease`` instead.
**min_impurity_decrease** (min_impurity_decrease)
    A node will be split if this split induces a decrease of the impurity
    greater than or equal to this value.

    The weighted impurity decrease equation is the following::

        N_t / N * (impurity - N_t_R / N_t * right_impurity
                            - N_t_L / N_t * left_impurity)

    where ``N`` is the total number of samples, ``N_t`` is the number of
    samples at the current node, ``N_t_L`` is the number of samples in the
    left child, and ``N_t_R`` is the number of samples in the right child.

    ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
    if ``sample_weight`` is passed.

    .. versionadded:: 0.19
**random_state** (random_state)
    If int, random_state is the seed used by the random number generator;
    If RandomState instance, random_state is the random number generator;
    If None, the random number generator is the RandomState instance used
    by `np.random`.
**warm_start** (warm_start)
    When set to ``True``, reuse the solution of the previous call to fit
    and add more estimators to the ensemble, otherwise, just fit a whole
    new forest. See warm_start.

.. automodule:: node_RandomForestClassifier

.. class:: RandomForestClassifier