clr算法python代码

CLR (Classification and Regression Tree) 算法是一种决策树的学习算法，常用于分类和回归分析。Python 中可以使用 Scikit-learn 库来实现这个算法。下面是一个简单的例子，展示了如何使用 `DecisionTreeClassifier` 或 `DecisionTreeRegressor` 类来创建分类或回归树：

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

# 假设我们有一个数据集df，包含特征X和目标变量y
data = pd.read_csv('your_data.csv') # 替换为实际数据文件路径
X = data.drop('target_column', axis=1) # 替换为目标列名
y = data['target_column']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建分类树
if 'classification' in str(y.dtype): # 检查y是否为分类任务
    clf = DecisionTreeClassifier()
    clf.fit(X_train, y_train)
    predictions = clf.predict(X_test)
else: # 如果是回归任务
    reg = DecisionTreeRegressor()
    reg.fit(X_train, y_train)
    predictions = reg.predict(X_test)

# 打印一些模型信息
print("Model:", type(clf))
print("Feature importances:", clf.feature_importances_ if isinstance(clf, DecisionTreeClassifier) else reg.feature_importances_)