Hi I am using boosting for a classification and prediction problem. For some reason it is giving me an outcome that doesn't fall between 0 and 1 for the predictions. I have tried type="response" but it made no difference. Can anyone see what I am doing wrong? Screen output shown below: > boost.model <- gbm(as.factor(train$simNuance) ~ ., # formula + data=train, # dataset + # +1: monotone increase, + # 0: no monotone restrictions + distribution="gaussian", # bernoulli, adaboost, gaussian, + # poisson, and coxph available + n.trees=3000, # number of trees + shrinkage=0.005, # shrinkage or learning rate, + # 0.001 to 0.1 usually work + interaction.depth=3, # 1: additive model, 2: two-way interactions, etc. + bag.fraction = 0.5, # subsampling fraction, 0.5 is probably best + train.fraction = 0.5, # fraction of data for training, + # first train.fraction*N used for training + n.minobsinnode = 10, # minimum total weight needed in each node + cv.folds = 5, # do 5-fold cross-validation + keep.data=TRUE, # keep a copy of the dataset with the object + verbose=FALSE) # print out progress > > best.iter = gbm.perf(boost.model,method="cv") > pred = predict.gbm(boost.model, test, best.iter) > summary(pred) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.4772 1.5140 1.6760 1.5100 1.7190 1.9420
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