ML Algos in R

I have been trying to understand how ML algos in R fit together / compare with each other.

# R RevoScaleR MML Comment
1 lm rxLinMod  — Linear models
2 glm rxGlm  — Linear models
3 Glm w/

Binomial family and the logit link function

rxLogit rxLogisticRegression Logistic regression
4 rpart rxDtree  — Decision Trees implementations
5 gbm rxBTrees rxFastTrees Boosted Decision Tree implementations
6  —- rxDForest rxFastForest

 

References:

# Title Links
1 Fitting Logistic Regression Models https://msdn.microsoft.com/en-us/microsoft-r/scaler-user-guide-logistic-regression
2 Generalized Linear Models https://msdn.microsoft.com/en-us/microsoft-r/scaler-user-guide-generalized-linear-mode
3 rxDTree(): a new type of tree algorithm for big data http://blog.revolutionanalytics.com/2013/07/rxdtree-a-new-type-of-tree-algorithm.html
4 A first look at rxBTrees http://blog.revolutionanalytics.com/2015/03/a-first-look-at-rxbtrees.html
5 A First Look at rxDForest() http://blog.revolutionanalytics.com/2014/01/a-first-look-at-rxdforest.html

 

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