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library(adaStump) | |
#Load Letters Dataset | |
letters.data <- read.csv("https://archive.ics.uci.edu/ml/machine-learning-databases/letter-recognition/letter-recognition.data", header = F) | |
names(letters.data) <- c("lettr", "xbox", "ybox", "width", | |
"high", "onpix", "xbar", "ybar", | |
"x2bar", "y2bar", "xybar", "x2ybr", | |
"xy2br", "xege", "xegvy", "yege", "yegvx") | |
#Create a binary clas | |
letters.data$isA <- ifelse(letters.data$lettr == "A","Yes","No") | |
#Create model formula. Exclude letter var for obvious reasons | |
antec <- paste0(setdiff(names(letters.data),c("isA","lettr")), collapse = "+") | |
fla <- as.formula(paste("isA", antec, sep = "~")) | |
#### Model training comparison #### | |
#ada model fit passing the corresponding control function to create Stumps | |
fit_with.ada <- ada(formula = fla,data = letters.data, type = "real", | |
control = rpart.control(maxdepth=1,cp=-1,minsplit=0,xval=0), iter = 40, nu = 0.05, bag.frac = 1) | |
#adaStump | |
fit_with.adaStump <- adaStump(formula = fla,data = letters.data, | |
type = "real", iter = 40, nu = 0.05, bag.frac = 1) | |
#Object size comparison | |
format(object.size(fit_with.ada), units = "KB") | |
format(object.size(fit_with.adaStump), units = "KB") | |
#### Model testing #### | |
system.time(predictions_ada <- predict(fit_with.ada, letters.data, type = "prob")) | |
system.time(predictions_adaStump <- predict(fit_with.adaStump, letters.data)) | |
#### Model pruning #### | |
fit_with.prunedadaStump <- pruneTree(fit_with.adaStump) | |
system.time(predictions_prunedadaStump <- predict(fit_with.prunedadaStump, letters.data)) |
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