假设我有以下DecisionTreeClassifier模型：

from sklearn.tree import DecisionTreeClassifierfrom sklearn.datasets import load_breast_cancerbunch = load_breast_cancer()X, y = bunch.data, bunch.targetmodel = DecisionTreeClassifier(random_state=100)model.fit(X, y)

我想遍历这棵树中的每个节点（包括叶节点和决策节点），并确定预测值在遍历过程中如何变化。基本上，我希望能够了解，对于给定的样本，最终的预测（由.predict返回）是如何确定的。比如，样本最终可能被预测为1，但在遍历了四个节点后，在每个节点上的“常数”（scikit文档中使用的术语）预测值从1变为0，再变为0，最后又变为1。

从model.tree_.value中获取这些信息的方法并不明显，其描述如下：

 |  value : array of double, shape [node_count, n_outputs, max_n_classes] |      Contains the constant prediction value of each node.

在该模型的情况下，看起来像这样：

>>> model.tree_.value.shape(43, 1, 2)>>> model.tree_.valuearray([[[212., 357.]],       [[ 33., 346.]],       [[  5., 328.]],       [[  4., 328.]],       [[  2., 317.]],       [[  1.,   6.]],       [[  1.,   0.]],       [[  0.,   6.]],       [[  1., 311.]],       [[  0., 292.]],       [[  1.,  19.]],       [[  1.,   0.]],       [[  0.,  19.]],

有谁知道我如何实现这个目标吗？对于上面43个节点中的每一个，其类别预测是否只是每个列表的argmax？所以从上到下是1, 1, 1, 1, 1, 1, 0, 0, …？

回答：

一种解决方案可能是直接遍历树中的决策路径。你可以改编这个解决方案，它将整个决策树打印为if语句。这里是快速改编以解释一个实例的代码：

def tree_path(instance, values, left, right, threshold, features, node, depth):    spacer = '    ' * depth    if (threshold[node] != _tree.TREE_UNDEFINED):        if instance[features[node]] <= threshold[node]:            path = f'{spacer}{features[node]} ({round(instance[features[node]], 2)}) <= {round(threshold[node], 2)}'            next_node = left[node]        else:            path = f'{spacer}{features[node]} ({round(instance[features[node]], 2)}) > {round(threshold[node], 2)}'            next_node = right[node]        return path + '\n' + tree_path(instance, values, left, right, threshold, features, next_node, depth+1)    else:        target = values[node]        for i, v in zip(np.nonzero(target)[1],                        target[np.nonzero(target)]):            target_count = int(v)            return spacer + "==> " + str(round(target[0][0], 2)) + \                   " ( " + str(target_count) + " examples )"def get_path_code(tree, feature_names, instance):    left      = tree.tree_.children_left    right     = tree.tree_.children_right    threshold = tree.tree_.threshold    features  = [feature_names[i] for i in tree.tree_.feature]    values = tree.tree_.value    return tree_path(instance, values, left, right, threshold, features, 0, 0)# print the decision path of the first intance of a panda dataframe dfprint(get_path_code(tree, df.columns, df.iloc[0]))