sklearn.ensemble之RandomForestClassifier源码解读（一）

class RandomForestClassifier(ForestClassifier)

    A random forest classifier.

    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=True` (default).

    # 将数据集（dataset）分成若干子集（sub-sample）
    # 每个子集作为一棵决策树（decision tree）的训练集（training data）
    # 参数 bootstrap 的值会影响到数据子集（sub-sample）的划分

---

参数（Parameters）:

[ bootstrap ] ==> boolean, optional (default=True)

    Whether bootstrap samples are used when building trees.

    # 构建树（即子分类器）的时候，样本选取是否采用有放回抽样。

[ criterion ] ==> string, optional (default=”gini”)

    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.

    # 不纯度判断标准，判断决策树节点是否需要继续分裂时采用的计算方法，
    # 默认是gini，可以修改为entropy。

[ max_features ] ==> int, float, string or None, optional (default=”auto”)

    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 percentage 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.

    # 节点分裂的时候，参与判断的最大特征数，默认是auto模式。
    #        int：个数
    #        float：占所有特征的百分比
    #        auto：所有特征数的开方
    #        sqrt：所有特征数的开方
    #        log2：所有特征数的log2值
    #        None：等于所有特征数

[ max_depth ] ==> integer or None, optional (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.

    # 表示树的最大深度，int型。
    # 默认是None，这时，树会生长到所有叶子节点都属于同一类，
    # 或者某节点所代表的样本数已经小于min_samples_split。

[ min_samples_split ] ==> int, float, optional (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 percentage and
          `ceil(min_samples_split * n_samples)` are the minimum
          number of samples for each split.

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

    # 叶子节点分裂所需要的最小样本数，int型，默认是2。
    # 版本0.18中添加了float型，即占所有样本数n_samples的比例。

[ min_samples_leaf ] ==> int, float, optional (default=1)

    The minimum number of samples required to be at a leaf node:
        - If int, then consider `min_samples_leaf` as the minimum number.
        - If float, then `min_samples_leaf` is a percentage and
          `ceil(min_samples_leaf * n_samples)` are the minimum
          number of samples for each node.

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

    # 叶子节点的最小样本数，int型，默认是1.
    # 版本0.18中添加了float型，即占所有样本数n_samples的比例。

[ min_weight_fraction_leaf ] ==> float, optional (default=0.)

    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.

    叶节点最小样本权重总值。
    不输入的话默认为0，即所有样本权重相等。

[ max_leaf_nodes ] ==> int or None, optional (default=None)

    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.

    # 最大叶节点数，int型；默认是None，即不限制叶节点数。

[ min_impurity_split ] ==> float

    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 and will be removed in 0.21.
       Use ``min_impurity_decrease`` instead.

    # 该参数代表树生长停止时的阈值，当impurity低于该值的时候，树生长停止。
    # 该属性将在0.21版本中删除，请改用``min_impurity_decrease``。

[ min_impurity_decrease ] ==> float, optional (default=0.)

    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
    # 该参数是在版本0.19中加入的。
    # 如果节点的分裂使得impurity值的减少大于或者等于该参数值，则节点分裂。
    #    N : 代表样本总数
    #    N_t : 代表当前节点的样本数
    #    N_t_L : 代表左孩子的样本数
    #    N_t_R : 代表右孩子的样本数
    #    如果``sample_weight``被传递的话，以上四个变量都是加权求和的值。

[ n_estimators ] ==> integer, optional (default=10)

    The number of trees in the forest.

    # RandomForestClassifier中子分类器的个数，整型，默认是10.

[ n_jobs ] ==> integer, optional (default=1)

    The number of jobs to run in parallel for both `fit` and `predict`.
    If -1, then the number of jobs is set to the number of cores.

    # 并行数，int型，默认是1。设置为-1的时候，说明并行数跟CPU核数一致。
    # 在函数`fit`和`predict`中用到。

[ oob_score ] ==> bool (default=False)

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

    # 是否使用OOB样本估计模型的精度。

    # 在RandomForest构建的过程中，训练集中的有些数据可能没有被选择，
    # 这些数据称为out-of-bag（OOB） examples。

[ random_state ] ==> int, RandomState instance or None, optional (default=None)

    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`.

    # 该参数用于设置随机器对象。
    #    int : 作为the random number generator的种子
    #    RandomState instance : 作为the random number generator
    #    None : 使用np.random作为the random number generator

[ verbose ] ==> int, optional (default=0)

    Controls the verbosity of the tree building process.

    # 日志冗长度。默认为0，表示不输出训练过程

[ warm_start ] ==> bool, optional (default=False)

    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.

    # 是否热启动。如果是，则下一次训练是以追加树的形式进行，
    # 重新使用上一次的解决方案，并将更多的子分类器添加进去。
    # 默认是``False``，即重新生成一个新的RandomForestClassifier。

[ class_weight ] ==> dict, list of dicts, “balanced”,

    "balanced_subsample" or None, optional (default=None)
    Weights associated with classes in the form ``{class_label: weight}``.

    If not given, all classes are supposed to have weight one. For
    multi-output problems, a list of dicts can be provided in the same
    order as the columns of y.

    Note that for multioutput (including multilabel) weights should be
    defined for each class of every column in its own dict. For example,
    for four-class multilabel classification weights should be
    [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
    [{1:1}, {2:5}, {3:1}, {4:1}].

    The "balanced" mode uses the values of y to automatically adjust
    weights inversely proportional to class frequencies in the input data
    as ``n_samples / (n_classes * np.bincount(y))``.
    The "balanced_subsample" mode is the same as "balanced" except that
    weights are computed based on the bootstrap sample for every tree
    grown.

    For multi-output, the weights of each column of y will be multiplied.
    Note that these weights will be multiplied with sample_weight (passed
    through the fit method) if sample_weight is specified.

    # 类别的权值。

---

属性（Attributes）:

[ estimators_ ] ==> list of DecisionTreeClassifier

        The collection of fitted sub-estimators.

        # 在RandomForestClassifier中，指的是决策树分类器的集合。

[ classes_ ] ==> array of shape = [n_classes] or a list of such arrays

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

        # 单个类别输出问题或者多类别输出问题中的类别标签数组。

[ n_classes_ ] ==> int or list

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

        # 单个类别输出问题或者多类别输出问题中的类别标签的个数。

[ n_features_ ] ==> int

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

        # 数据集的特征个数，整型。

[ n_outputs_ ] ==> int

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

        # 输出的个数，整型 ==> self.n_outputs_ = y.shape[1]
        # 可以在程序当中用来鉴别要输出的是一个数还是一个array。

[ feature_importances_ ] ==> array of shape = [n_features]

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

[ oob_score_ ] ==> float

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

        # 在RandomForest构建的过程中，训练集中的有些数据可能没有被选择，
        # 这些数据称为out-of-bag（OOB） examples。
        # 这些数据由于没有用来训练模型，故可以用于模型的验证。

[ oob_decision_function_ ] ==> array of shape = [n_samples, n_classes]

        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.

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sklearn源码详见： https://github.com/scikit-learn/scikit-learn/tree/master/sklearn/ensemble

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