搜索资源列表
DT
- 关于数据挖掘中的决策树ID3算法的代码。-About Data Mining ID3 decision tree algorithm code.
C4.5
- C4.5版本决策树程序源代码，简单易懂，队初学数据挖掘者有个很好的参考价值。-C4.5 decision tree version of the source code, easy to understand, the team novice data miners have a good reference value.
RandomForest
- 基于决策树实现的随机森林算法，是数据挖掘中一个经典的分类算法，对初学者具有很好的学习参考意义。-Based on Decision Tree achieved random forests algorithm is a classical data mining classification algorithm, for beginners to learn with a good reference value.
Id3
- web数据挖掘技术的决策树算法ID3的Java源代码。-web data mining technology ID3 decision tree algorithm Java source code. . . . . . .
Trees-J48-Java-Code
- 决策树算法J48的Java源程序，十大经典算法之一。eclipse环境下导入算法可进行数据分类。-J48 decision tree algorithm of Java source code, one of the top ten classical algorithm. eclipse environment can import data classification algorithms.
C45Test
- 决策树java实现，包括工程，以及源代码，直接打开就可以使用。-decision tree
C4.5_Source
- 使用纯JAVA语言原生开发C4.5决策树算法，没有使用工具包，并且通过界面展示了生成的决策树图像。-Using pure JAVA language Native Development C4.5 decision tree algorithm, do not use the toolkit, and by interface shows the image generated by the decision tree.
jbrt-master
- 机器学习算法中的迭代决策树算法，是一种用途非常广泛的多决策树算法，可以有效的解决问题-Machine learning algorithms in iterative decision tree algorithm, is a very versatile multi-tree algorithm can effectively solve the problem
source
- 对两个数据集分别建立决策树，并运用自适应算法和随机森林，并验证准确率-Of the two data sets were established decision tree, and the use of adaptive algorithms and random forest, and verify accuracy
ID3-java
- 这是模式识别中的决策树那一章中的ID3算法，包含很多文件-This is a pattern recognition decision tree ID3 algorithm of the chapter, contains many files
moh
- Example for GENERATE DECISION TREE of animal
src.tar
- c45算法java实现,机器学习决策树算法的简单实现-c45 algorithm to achieve the java machine learning decision tree algorithm simple implementation
ID3
- Java实现决策树方法 ID3算法的简单例子-Java ID3 decision tree algorithm is simple example
C4.5
- 数据挖掘 决策树算法 C4.5 JAVA编程实现 GUI界面显示决策树的分析结果-DataMining decision tree C4.5 in java code GUI interface to show the result of decision tree
ID3
- ID3决策树的建立，直接在eclipse上运行-ID3 decision tree establishment, run directly in the eclipse
RandomForest
- 随机森林是由多棵树组成的分类或回归方法。主要思想来源于Bagging算法，Bagging技术思想主要是给定一弱分类器及训练集，让该学习算法训练多轮，每轮的训练集由原始训练集中有放回的随机抽取，大小一般跟原始训练集相当，这样依次训练多个弱分类器，最终的分类由这些弱分类器组合，对于分类问题一般采用多数投票法，对于回归问题一般采用简单平均法。随机森林在bagging的基础上，每个弱分类器都是决策树，决策树的生成过程中中，在属性的选择上增加了依一定概率选择属性，在这些属性中选择最佳属性及分割点，传统做法
JavaC4.5
- Data mining C4.5 Decision Tree Algorithm on Java
DecisionTree
- 机器学习算法决策树算法中ID3算法的简单实现代码-Machine learning algorithms decision tree algorithm ID3 algorithm implementation code simple
决策树（java）variance 回归
- mllib调用决策树的java代码，希望对大家有帮助(Mllib calls the Java code of the decision tree)
决策树C4.5
- 利用决策树训练训练器 分辨 有毒蘑菇和无毒蘑菇 java书写，包含训练集及说明 代码无bug(Using decision tree training trainer to distinguish poisonous mushroom and non-toxic mushroom Java writing, including training set and explanation code.)