机器学习-随机森林回归(Random Forest Regression)

最新推荐文章于 2025-09-04 15:56:04 发布

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最新推荐文章于 2025-09-04 15:56:04 发布 · 1.2w 阅读

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#python #机器学习

本文介绍了随机森林回归，一种结合多个决策树的集成技术。随机森林由于其随机性，通常具有更好的泛化性能，对数据集中的异常值不敏感，并且参数调优需求较小，主要参数为树的数量。预测结果通过所有决策树的平均预测得出。

Section I: Brief Introduction on Random Forest Regression

The random forest algorithm is an ensemble technique that combines multiple decision trees. A random forest usually has a better generalization performance than an individual tree due to randomness, which helps to decrease the model’s varaiance. Other advantages of random forests are that they are less sensitive to outliers in the dataset and don’t require much parameter tuning. The only parameter in random forests that we typically need to experiment with is the number of trees in the ensemble. The only difference is that we use the MSE criterion to grow the individual decision trees, and the predicted target variable is calculated as the average prediction over all decision trees.

FROM
Sebastian Raschka, Vahid Mirjalili. Python机器学习第二版. 南京：东南大学出版社，2018.

代码

from sklearn import datasets
import matplotlib.pyplot as plt
import numpy as np
import warnings
warnings.filterwarnings("ignore")

plt.rcParams['figure.dpi']=200
plt.rcParams['savefig.dpi']=200
font = {
   
   'weight': 'light'}
plt.rc("font"