/play_with_machine_learning

numpy实现常用的的机器学习库,分类模型实现:KNN，LDA，LR，Decision Tree(ID3,C4.5,CART)，RF，perception，SVM，Neural network，GBDT，Xgboost，Adaboost;回归模型实现 :LASSO，Ridge，LR，RF，Decision Tree(ID3,C4.5,CART);非监督模型 :PCA，Kmeans，SVD;概率模型 :MEM，HMM,CRF,EM,NB,MCMC

Primary LanguagePython

play_with_machine_learning

numpy实现的机器学习库

分类模型

  • KNN
  • LDA
  • LR
  • Decision Tree(ID3,C4.5,CART)
  • RF
  • perception
  • SVM
  • Neural network
  • GBDT
  • Xgboost
  • Adaboost

回归模型

  • LASSO
  • Ridge
  • LR
  • RF
  • Decision Tree(ID3,C4.5,CART)
  • GBDT
  • Xgboost

非监督模型

  • PCA
  • Kmeans
  • SVD
  • LDA(topic model)

概率模型

  • ME
  • GMM
  • HMM
  • CRF
  • EM
  • NB
  • MCMC

Bandit模型簇

  • MAB: Bernoulli, Multinomial, and Gaussian payout distributions
  • Contextual MAB: Linear contextual bandits
  • Epsilon-greedy
  • UCB1 (Auer, Cesa-Bianchi, & Fisher, 2002)
  • Conjugate Thompson sampler for Bernoulli bandits (Thompson, 1933; Chapelle & Li, 2010)
  • LinUCB (Li, Chu, Langford, & Schapire, 2010)

因子模型

  • Regularized alternating least squares (ALS)
  • Non-negative matrix factorization via fast hierarchical least squares (HALS)
  • SVD