Cheat Sheet for Prediction and Classification Models in R

Ricky Ho has created a reference a 6-page PDF reference card on Big Data Machine Learning, with examples implemented in the R language. (A free registration to DZone Refcardz is required to download the PDF.) The examples cover:

Predictive modeling overview (how to set up test and training sets in R)
Linear regression (using lm)
Logistic regression (using glm)
Regression with regularization (using the glmnet package)
Neural networks (using nnet)
Support vector machines (using tune.svm from the e1071 package)
Naïve Bayes models (using naiveBayes from the e1071 package)
K-nearest-neighbors classification (using the knn function from the class package)
Decision trees (using rpart)
Ensembles of trees (using the randomForest package)
Gradient boosting (using the gbm package)

The examples use the traditional built-in R data sets (such as the iris data, used to create the neural network above), so there’s unfortunately not much of a “big data” aspect to the reference card. But if you’re just getting started with prediction and classification models in R, this cheat sheet is a useful guide.