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R语言机器学习包  

2012-03-15 08:52:11|  分类: R |  标签: |举报 |字号 订阅

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R语言中的机器学习包

Machine Learning & Statistical Learning (机器学习 & 统计学习) 
网址:http://cran.r-project.org/web/views/MachineLearning.html维护人员:Torsten Hothorn 
版本:2008-02-18 18:19:21 
翻译:R-fox, 2008-03-18 

机器学习是计算机科学和统计学的边缘交叉领域,R关于机器学习的包主要包括以下几个方面: 
1)神经网络(Neural Networks): 
nnet包执行单隐层前馈神经网络,nnet是VR包的一部分(http://cran.r-project.org/web/packages/VR/index.html)。 
2)递归拆分(Recursive Partitioning): 
递归拆分利用树形结构模型,来做回归、分类和生存分析,主要在rpart包(http://cran.r-project.org/web/packages/rpart/index.html)和tree包(http://cran.r-project.org/web/packages/tree/index.html)里执行,尤其推荐rpart包。Weka里也有这样的递归拆分法,如:J4.8, C4.5, M5,包Rweka提供了R与Weka的函数的接口(http://cran.r-project.org/web/packages/RWeka/index.html)。 
party包提供两类递归拆分算法,能做到无偏的变量选择和停止标准:函数ctree()用非参条件推断法检测自变量和因变量的关系;而函数mob()能用来建立参数模型(http://cran.r-project.org/web/packages/party/index.html)。另外,party包里也提供二分支树和节点分布的可视化展示。 
mvpart包是rpart的改进包,处理多元因变量的问题(http://cran.r-project.org/web/packages/mvpart/index.html)。rpart.permutation包用置换法(permutation)评估树的有效性(http://cran.r-project.org/web/packages/rpart.permutation/index.html)。knnTree包建立一个分类树,每个叶子节点是一个knn分类器(http://cran.r-project.org/web/packages/knnTree/index.html)。LogicReg包做逻辑回归分析,针对大多数自变量是二元变量的情况(http://cran.r-project.org/web/packages/LogicReg/index.html)。maptree包(http://cran.r-project.org/web/packages/maptree/index.html)和pinktoe包(http://cran.r-project.org/web/packages/pinktoe/index.html)提供树结构的可视化函数。 
3)随机森林(Random Forests): 
randomForest 包提供了用随机森林做回归和分类的函数(http://cran.r-project.org/web/packages/randomForest/index.html)。ipred包用bagging的思想做回归,分类和生存分析,组合多个模型(http://cran.r-project.org/web/packages/ipred/index.html)。party包也提供了基于条件推断树的随机森林法(http://cran.r-project.org/web/packages/party/index.html)。varSelRF包用随机森林法做变量选择(http://cran.r-project.org/web/packages/varSelRF/index.html)。 
4)Regularized and Shrinkage Methods: 
lasso2包(http://cran.r-project.org/web/packages/lasso2/index.html)和lars包(http://cran.r-project.org/web/packages/lars/index.html)可以执行参数受到某些限制的回归模型。elasticnet包可计算所有的收缩参数(http://cran.r-project.org/web/packages/elasticnet/index.html)。glmpath包可以得到广义线性模型和COX模型的L1 regularization path(http://cran.r-project.org/web/packages/glmpath/index.html)。penalized包执行lasso (L1) 和ridge (L2)惩罚回归模型(penalized regression models)(http://cran.r-project.org/web/packages/penalized/index.html)。pamr包执行缩小重心分类法(shrunken centroids classifier)(http://cran.r-project.org/web/packages/pamr/index.html)。earth包可做多元自适应样条回归(multivariate adaptive regression splines)(http://cran.r-project.org/web/packages/earth/index.html)。 
5)Boosting : 
gbm包(http://cran.r-project.org/web/packages/gbm/index.html)和boost包(http://cran.r-project.org/web/packages/boost/index.html)执行多种多样的梯度boosting算法,gbm包做基于树的梯度下降boosting,boost包包括LogitBoost和L2Boost。GAMMoost包提供基于boosting的广义相加模型(generalized additive models)的程序(http://cran.r-project.org/web/packages/GAMMoost/index.html)。mboost包做基于模型的boosting(http://cran.r-project.org/web/packages/mboost/index.html)。 
6)支持向量机(Support Vector Machines): 
e1071包的svm()函数提供R和LIBSVM的接口 (http://cran.r-project.org/web/packages/e1071/index.html)。kernlab包为基于核函数的学习方法提供了一个灵活的框架,包括SVM、RVM……(http://cran.r-project.org/web/packages/kernlab/index.html) 。klaR 包提供了R和SVMlight的接口(http://cran.r-project.org/web/packages/klaR/index.html)。 
7)贝叶斯方法(Bayesian Methods): 
BayesTree包执行Bayesian Additive Regression Trees (BART)算法(http://cran.r-project.org/web/packages/BayesTree/index.htmlhttp://www-stat.wharton.upenn.edu/~edgeorge/Research_papers/BART%206--06.pdf)。tgp包做Bayesian半参数非线性回归(Bayesian nonstationary, semiparametric nonlinear regression)(http://cran.r-project.org/web/packages/tgp/index.html)。 
8)基于遗传算法的最优化(Optimization using Genetic Algorithms): 
gafit包(http://cran.r-project.org/web/packages/gafit/index.html)和rgenoud包(http://cran.r-project.org/web/packages/rgenoud/index.html)提供基于遗传算法的最优化程序。 
9)关联规则(Association Rules): 
arules包提供了有效处理稀疏二元数据的数据结构,而且提供函数执Apriori和Eclat算法挖掘频繁项集、最大频繁项集、闭频繁项集和关联规则(http://cran.r-project.org/web/packages/arules/index.html)。 
10)模型选择和确认(Model selection and validation): 
e1071包的tune()函数在指定的范围内选取合适的参数(http://cran.r-project.org/web/packages/e1071/index.html)。ipred包的errorest()函数用重抽样的方法(交叉验证,bootstrap)估计分类错误率(http://cran.r-project.org/web/packages/ipred/index.html)。svmpath包里的函数可用来选取支持向量机的cost参数C(http://cran.r-project.org/web/packages/svmpath/index.html)。ROCR包提供了可视化分类器执行效果的函数,如画ROC曲线(http://cran.r-project.org/web/packages/ROCR/index.html)。caret包供了各种建立预测模型的函数,包括参数选择和重要性量度(http://cran.r-project.org/web/packages/caret/index.html)。caretLSF包(http://cran.r-project.org/web/packages/caretLSF/index.html)和caretNWS(http://cran.r-project.org/web/packages/caretNWS/index.html)包提供了与caret包类似的功能。 
11)统计学习基础(Elements of Statistical Learning): 
书《The Elements of Statistical Learning: Data Mining, Inference, and Prediction 》(http://www-stat.stanford.edu/~tibs/ElemStatLearn/)里的数据集、函数、例子都被打包放在ElemStatLearn包里(http://cran.r-project.org/web/packages/ElemStatLearn/index.html)。

ahaz  http://cran.r-project.org/web/packages/ahaz/index.html Computationally efficient procedures for regularized estimation with the semiparametric additive hazards regression model.
arules (frequent itemsets and association rules) http://cran.r-project.org/web/packages/arules/index.html
BayesTree  BART:Bayesian Additive Regression Trees  http://cran.r-project.org/web/packages/BayesTree/index.html
Boruta   feature selection algorithm based on a randomForest classifier
BPHO Bayesian Prediction with High-order Interactions
bst Gradient Boosting
caret  Classification and Regression Training
CORElearn classification, regression, feature evaluation and ordinal evaluation
CoxBoost  Cox models by likelihood based boosting for a single survival endpoint or competing risks
Cubist  Rule- and Instance-Based Regression Modeling
e1071 (core) Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier
earth  Multivariate Adaptive Regression Spline Models
elasticnet Elastic-Net for Sparse Estimation and Sparse PCA
ElemStatLearn
evtree  Evolutionary Learning of Globally Optimal Trees
gafit Genetic Algorithm for Curve Fitting
GAMBoost Generalized linear and additive models by likelihood based boosting
gamboostLSS
gbev
gbm (core)
glmnet
glmpath
GMMBoost
grplasso Fitting user specified models with Group Lasso penalty
hda
ipred
kernlab (core)
klaR
lars
lasso2
LiblineaR
LogicForest
LogicReg
longRPart
mboost (core)
mvpart
ncvreg
nnet (core)
oblique.tree
obliqueRF
pamr
party
partykit
penalized
penalizedSVM
predbayescor
quantregForest
randomForest (core)
randomSurvivalForest
rattle
rda
rdetools
REEMtree
relaxo
rgenoud
rgp
rminer
ROCR
rpart (core)
rpartOrdinal
RPMM
RSNNS
RWeka
sda
SDDA
svmpath
tgp
tree
TWIX
varSelRF
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