DecisionTreeClassifier — scikit-learn 1.6.1 documentation
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. See also DecisionTreeRegressor
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决策树模型详解-CSDN博客
2020年6月18日 · 决策树（Decision Tree）是一种非参数的有监督学习方法，它能够从一系列有特征和标签的数据中总结出决策规则，并用树状图的结构来呈现这些规则，以解决分类和回归问 …
sklearn决策树可视化以及输出决策树规则 - CSDN博客
将sklearn的决策树规则输出成SQL 主要使用sklearn.tree._tree 读取决策树的信息 1、输出成SQL的主要函数 import numpy as np from sklearn.tree import DecisionTreeClassifier from …
1.10. Decision Trees — scikit-learn 1.6.1 documentation
Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by …
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一文读懂sklearn决策树参数详解（python代码） - CSDN博客
2024年6月8日 · 本文详细解读了sklearn决策树的完整参数配置，包括常用参数如class_weight、ccp_alpha，以及防止过拟合的min_samples_leaf等。 通过实例演示如何训练模型、预测及评 …
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一个超强算法模块：sklearn.tree！！ - 知乎 - 知乎专栏
scikit-learn 的决策树模块支持以下两种主要的问题类型：1、 分类问题（Classification）：用于将数据点分为不同的类别。 决策树通过选择特征来进行分裂，并最终将数据点分到相应的类别 …
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