CART是分类与回归树（Classification and Regression Trees, CART），是一棵二叉树，可用于回归与分类。
下面是分类树：
class sklearn.tree.DecisionTreeClassifier(criterion=’gini’, splitter=’best’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None, presort=False)
Parameters:
1）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.
衡量生成树的纯度，可选择基尼系数 'gini'，或者信息熵 'entropy'.
节点 t 的基尼系数与信息熵计算公式：
2）splitter : string, optional (default=”best”)
The strategy used to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split.
分裂点选择，有两种方式可选择，'best' 与 'random'。
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3）max_depth : int 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.
设置决策树的最大深度。如果为None，停止条件为：
1）所有叶子节点不纯度为0
2）叶子节点包含样本个数低于 'min_samples_split'
4）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 fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.
Changed in version 0.18: Added float values for fractions.
内部节点包含的最少样本数，可输入整数或者小数；即如果内部节点包含样本书低于这个值，则不再分裂，直接作为叶子节点。
如果输入整数 d，min_samples_split = d;
如果输入小数 f ， min_samples_split = f * N；N为样本总数。
5）min_samples_leaf : int, float, optional (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.
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.
Changed in version 0.18: Added float values for fractions.
叶子节点包含的最少样本数。
如果一个节点分裂后，左右子节点包含样本书低于min_samples_leaf，则不可以分裂。
6）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.
叶子节点包含样本的最小权重值。
7）max_features : int, float, string or None, optional (default=None)
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).
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.
节点分裂时考虑的最大特征数量。
8）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.
随机种子/随机生成器。
9）max_leaf_nodes : int or None, optional (default=None)
Grow a tree 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.
叶子节点最大个数。
10）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.
New in version 0.19.
不纯度最小减少值，即分裂节点时必须满足减少的不纯度大于这个值。
如果设置了样本权重，N, N_t, N_t_R and N_t_L指的是加权和，否则只是样本计数。
11）min_impurity_split : float, (default=1e-7)
Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.
Deprecated since version 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 1 e-7 to 0 in 0.23 and it will be removed in 0.25. Use min_impurity_decrease instead.
节点停止分裂的不纯度阈值。与上一参数作用相同。0.25版本后删除。
12）class_weight : dict, list of dicts, “balanced” or None, 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))
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.
设置类别权重，参数 dict, list of dicts，'balanced' 或者 'None'
输入list, list of dict，手动设置类别权重。
输入 'balanced', 模型利用公式 n_samples / (n_classes * np.bincount(y)) 对每个类别样本数量自动调整权重，使得每个类别的权重相同。即某个类别样本数量少，单个样本权重就大；类别样本数量多，单个样本权重就小。最终所有类别的权重都一样。当样本分布不均时，可以这样操作。
输入 'None', 默认样本权重都一样。
13）presort : bool, optional (default=False)
Whether to presort the data to speed up the finding of best splits in fitting. For the default settings of a decision tree on large datasets, setting this to true may slow down the training process. When using either a smaller dataset or a restricted depth, this may speed up the training.
是否对样本进行预分类。
即节点寻找最优分裂点之前，对样本进行预排序，加快找到最优分裂点；由于增加了一步操作，数据集小的时候可以加快速度；但数据集大的时候反而会减慢速度。
Attributes:
1）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).
标签列表
2）feature_importances_ : array of shape = [n_features]
Return the feature importances.
特征重要系数
3）max_features_ : int,
The inferred value of max_features.
特征数量
4）n_classes_ : int or list
The number of classes (for single output problems), or a list containing the number of classes for each output (for multi-output problems).
5）n_features_ : int
The number of features when fit is performed.
训练时用到的特征数量
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6）n_outputs_ : int
The number of outputs when fit is performed.
7）tree_ : Tree object
The underlying Tree object. Please refer to help(sklearn.tree._tree.Tree) for attributes of Tree object and Understanding the decision tree structure for basic usage of these attributes.
返回决策树对象（sklearn.tree._tree.Tree）
