搜索资源列表
Ch03
- 实现决策树的构建，在Python中使用matplotlib注解绘制(Implement the construction of the decision tree, used in Python, matplotlib annotation draw)
CART
- 决策树CART算法源代码，可利用，包括make_tree和use_tree(the code of decision tree CART algorithm including make_tree and use_tree)
id3决策树——new
- 这是一个ID3决策树分类的代码用于分类器的计算，大家下载类以后可以学习使用，ID3是一种比较古老的决策树，最新的有C4.5等(This is an ID3 decision tree classification code for classifier calculation, which can be used after downloading the class. ID3 is a relatively ancient decision tree, with the latest C4.5
Iris_DecisionTree
- python 鸢尾花（iris）数据分类测试程序，采用决策树方法。(Python iris flower (IRIS) data classification test program, using the decision tree method.)
gcForest-master
- 基于决策树构建深度森林模型实现较高特征表示能力相比深度卷积神经网络(Building deep forest model based on decision tree to achieve higher feature representation ability compared with deep convolution neural network)
decision_tree
- 以隐形眼镜为例的决策树分类算法代码，lenses.data是存放数据的文件，此代码使用python3实现(The classification algorithm code of the decision tree with the contact lens as an example)
决策树
- 数据处理中经常使用决策树算法，在MATLAB中编辑决策树算法(Decision tree algorithm is often used in data processing, and the decision tree algorithm is edited in MATLAB)
ID3决策树
- Java实现用ID3算法构建决策树(Java implementation using ID3 algorithm to build decision tree)
决策树java代码1
- 决策树可对数据进行分类，选出最大增益属性。(Decision tree can be classified data, select the maximum gain attribute.)
rseslib-3.0.4-src
- 包含很多知名算法实现，支持向量机，决策树，粗糙集，贝叶斯分类器等，适合学术研究，短评论意见挖掘，文本分类等(It includes many well-known algorithm implementation, support vector machine, decision tree, rough set, Bias classifier, etc., which is suitable for academic research, short comment mining, text c
decision_tree
- 决策树是一个利用像树一样的图形或决策模型的决策支持工具，包括随机事件结果，资源代价和实用性。(A decision tree is a decision support tool that uses a tree like graph or a decision model, including the result of random events, the cost of resources and the practicality.)
机器学习常用方法
- 机器学习常用方法的python实现，包括PCA，随机森林，决策树，层次聚类，kmeans，KNN，线性感知机等(Python implementation of common machine learning methods, including PCA, random forest, decision tree, hierarchical clustering, kmeans, KNN, linear perceptron, etc.)
BreastCancer
- Java实现机器学习经典分类算法，代码中实现了决策树、贝叶斯和KNN三个分类算法(Java implements the classic classification algorithm for machine learning. The code implements three classification algorithms: decision tree, Bayes and KNN)
决策树与随机森林
- 给出对决策树与随机森林的认识。主要分析决策树的学习算法：信息增益和ID3、C4.5、CART树，然后给出随机森林。 决策树中，最重要的问题有3个: 1. 特征选择。即选择哪个特征作为某个节点的分类特征； 2. 特征值的选择。即选择好特征后怎么划分子树； 3. 决策树出现过拟合怎么办？ 下面分别就以上问题对决策树给出解释。决策树往往是递归的选择最优特征，并根据该特征对训练数据进行分割。(The understanding of decision tree and random
C4.5-master
- source code for decision tree algorithm c4.5
j
- 决策树、代价敏感矩阵决策树实现(Decision tree and cost sensitive matrix decision tree)
08 决策树与随机森林
- 决策树和决策森林的代码，很不错，适合初学者，一起交流一起进步(The decision tree and decision forest code, very good, suitable for beginners, together with the progress of communication)
A01_fittree_csi_mt
- 有一个多分类的demo，运用决策树解决多分类问题，(Using decision tree to solve multi classification problem)
5
- 本程序论述在MATLAB环境下如何实现神经网络，包括了常用的神经网络及相关理论，如BP神经 网络、RBF神经网络、SVM、SOM神经网络、灰色神经网络、决策树、随机森林、小波神经网络、NARX神经网络等以及各种优化算法与神经网络的结合。(This procedure describes how to realize neural network in MATLAB environment, including the commonly used neural network and relate
机器学习决策树算法
- 此处python实现机器学习的决策树算法(A decision tree algorithm for realizing machine learning in Python)