LeBron-Jian / Machinelearningnote
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MachineLearningNote
因为下面所有的机器学习代码均使用了sklearn,这里也补充了一下Sklearn的学习博客:
- Python机器学习笔记:sklearn库的学习
- Python机器学习笔记:使用sklearn做特征工程和数据挖掘
- Python机器学习笔记:Grid SearchCV(网格搜索)
1,logistic Regression
关于逻辑回归文件夹中的数据和代码,详情请参考博客:
- Python机器学习笔记:Logistic Regression
2,Decision Tree
关于决策树文件夹中的数据和代码,详情请参考博客:
- python机器学习笔记:深入学习决策树算法原理
- python机器学习笔记:ID3决策树算法实战
3,K-NearestNeighbor(KNN)
关于K近邻文件夹中的代码和数据,详情请参考博客:
- Python机器学习笔记:K-近邻(KNN)算法
4,Naive Bayes
关于朴素贝叶斯文件夹中的代码,详情请参考博客:
- Python机器学习笔记:朴素贝叶斯算法
5,K-Means&DBSCAN
关于K-Means&DBSCAN文件夹中的代码和数据,详情请参考博客:
- Python机器学习笔记:K-Means算法,DBSCAN算法
6,Ensemble Learning
关于集成学习文件夹中的代码,详情请参考博客:
- Python机器学习笔记 集成学习总结
7,One Class SVM
关于单样本分类文件夹中的代码,详情请参考博客:
- Python机器学习笔记:One Class SVM
8,PCA
关于PCA降维算法文件夹中的代码,详情请参考博客:
- Python机器学习笔记:主成分分析(PCA)算法
9,LDA
关于LDA降维文件夹中的代码,详情请参考博客:
- Python机器学习笔记:线性判别分析(LDA)算法
10,EM(GMM)
关于EM算法文件夹中的代码,详情请参考博客:
- python机器学习笔记:EM算法
11,SVM
关于SVM算法文件夹中的代码,详情请参考博客:
- Python机器学习笔记:SVM(4)——sklearn实现
12,XGBoost
关于XGBoost算法文件夹中的代码,详情请参考博客:
- Python机器学习笔记:XgBoost算法
13,IsolationForest
关于IsolationForest算法文件夹中的代码,详情请参考博客:
- Python机器学习笔记:异常点检测算法——Isolation Forest
14,RamdomForest
关于RamdomForest算法文件夹中的代码,详情请参考博客:
- Python机器学习笔记:随机森林算法
15,Local Outlier Factor(LOF)
关于 Local Outlier Factor(LOF) 算法文件夹中的代码,详情请参考博客:
- Python机器学习笔记:异常点检测算法——LOF(Local Outiler Factor)
16,SVD
关于 SVD 算法文件夹中的代码,详情请参考博客:
- Python机器学习笔记:奇异值分解(SVD)算法
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