## ----setup, message=FALSE----------------------------------------------------- library(MLInterfaces) library(gbm) getClass("learnerSchema") ## ----lkrf--------------------------------------------------------------------- randomForestI@converter ## ----lknn--------------------------------------------------------------------- nnetI@converter ## ----lkknn-------------------------------------------------------------------- knnI(k=3, l=2)@converter ## ----show, message=FALSE------------------------------------------------------ library(MASS) data(crabs) kp = sample(1:200, size=120) rf1 = MLearn(sp~CL+RW, data=crabs, randomForestI, kp, ntree=100) rf1 RObject(rf1) knn1 = MLearn(sp~CL+RW, data=crabs, knnI(k=3,l=2), kp) knn1 ## ----mkadaI, message=FALSE---------------------------------------------------- adaI = makeLearnerSchema("ada", "ada", standardMLIConverter ) arun = MLearn(sp~CL+RW, data=crabs, adaI, kp ) confuMat(arun) RObject(arun) ## ----lks---------------------------------------------------------------------- standardMLIConverter ## ----lkggg-------------------------------------------------------------------- gbm2 ## ----tryg--------------------------------------------------------------------- BgbmI set.seed(1234) gbrun = MLearn(sp~CL+RW+FL+CW+BD, data=crabs, BgbmI(n.trees.pred=25000,thresh=.5), kp, n.trees=25000, distribution="bernoulli", verbose=FALSE ) gbrun confuMat(gbrun) summary(testScores(gbrun))