```matlab 加载数据 data = load('data.csv');提取特征 X = data.Features; Y = data.Target;计算信息增益 info_gain = infogain(X, Y);计算基尼不纯度 gini = gini(X, Y);计算信息增益比 info_ratio = infogain / gini;选择最优特征 best_feature = X(info_ratio > 0.5);构建决策树 tree = ...
matlab decisionTree for classification. Contribute to qinxiuchen/matlab-decisionTree development by creating an account on GitHub.
ID3-Decision-TreeEn**gy 上传7.09 KB 文件格式 zip ID3是一种决策树算法,用于分类任务。它基于信息熵和信息增益的概念,通过计算每个属性的信息增益来选择最佳分裂属性,构建决策树。本文描述了一个基于MATLAB实现的ID3算法,该算法可以处理包含离散值和连续值属性的数据集。具体实现过程包括:计算每个属性的信息增益,...
DecisionTree.zip Ir**ri上传11.98 KB文件格式zip 构造ID3算法决策树,用matplotlib绘制决策树 (0)踩踩(0) 所需:1积分 MovieProject 2025-02-08 00:45:21 积分:1 Android APP:HAC(活字格安卓容器) 2025-02-08 00:37:08 积分:1 pyqt6-channel-tool...
MATLAB Online에서 열기 I am not sure what exactly you want but the following code can be useful considering that you saved the decision tree as "tc". 테마복사 CP = tc.CutPoint; NC = tc.NodeClass; for ii = 1:size(CP,1) if ~isnan(CP(ii)) fprintf('if x%d <...
generating autolisp code to achieve the diagram drawing of variable id3 genotyping decision tree classifier by compiling of matlab matlab编译生成autolisp代... 图的绘制是非常必要的.本研究从分子生物学领域中的基因分型决策树绘制为实例,浅谈如何使用MALAB语言编译生成AUTOLISP代码,从而实现可变ID3基因分型决策树...
I understand that you want to compute an accuracy of decision tree using cross validation model. In MATLAB, you can compute the accuracy of a decision tree model using cross-validation and evaluate it using different metrics, including the ‘classperf’ method. You can follow the below steps:
If not, then follow the right branch to see that the tree classifies the data as type 1. To learn how to prepare your data for classification or regression using decision trees, see Steps in Supervised Learning. Train Classification Tree Copy Code Copy Command This example shows how to train...
the term decision tree is often reserved for trees used as classification models and used as a synonym of classification tree, while the term regression tree is used for trees used as regression models; for concreteness, we have said above that the sample mean of on a sub-group is used ...
1、决策树(decision tree)算法 决策树(decision tree)算法是根据给定的训练数据集构建一个决策树模型,使它能够对实例进行正确地分类,本质是从训练集中归纳出一组分类规则。决策树生成方法包括ID3、C4.5及CART等。 优点: 易于理解和解释,决策树可以可视化。 几乎不需