Question

我正在使用scikit-learn构建决策树模型，之后我想进行一些叶重写。基本上，我想更改特定叶节点的标签。

我在树叶上循环，并根据 tree.DecisionTreeClassifier.tree_ 我可以得到 tree_.value 为了计算节点的标签。我是从here获得的。我的问题是，是否可以以及如何强制更改决策树节点的标签？

目前，我尝试手动更改tree_.value

中的值

from sklearn import tree
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np

df = pd.read_csv("voting.csv", header=0)
y = pd.DataFrame(df.target)
feature_names = []
for col in df.columns:
    if col != 'target':
        feature_names.append(col)

y = df.target
df = df.drop("target", 1)

thr = 0.9
X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.2)
clf = tree.DecisionTreeClassifier(min_samples_leaf=3)
clf.fit(X_train, y_train)

node_count = clf.tree_.node_count
class_label = 0
for index in range(node_count):
    # check if it is a leaf
    if clf.tree_.children_right[index] == -1 and clf.tree_.children_left[index] == -1:
    # number of samples in the leaf (correctly classified and misclassified)
    print("Values: ", clf.tree_.value[index])
    # Finding node label
    node_label = clf.classes_[np.argmax(clf.tree_.value[index])]
    values = clf.tree_.value[index]
    correct_samples = values[0][node_label]
    misclassified_samples = np.sum(clf.tree_.value[index]) - correct_samples
    # Change the label if number of misclassified samples is more than 0
    if misclassified_samples > 0 and node_label != class_label:
        clf.tree_.value[index][class_label] = clf.tree_.value[index][class_label] + correct_samples
        print("New values: ", clf.tree_.value[index])

但这会导致更改两个值，即使正确分类也是如此。然后，节点的标签保持不变。例如，在操作之前： Values: [[1. 2.]] 以及手术后： New values: [[3. 4.]]

谢谢！

是否可以在sklearn决策树中更改节点的标签？

0 个答案: