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### RANDOM FOREST
# classification and regression
# continous and discrete data
# NA deleted
random.forest.importance <- function(formula, data, importance.type = 1) {
new_data = get.data.frame.from.formula(formula, data)
# get rid of NAs
no_na = rep(TRUE, dim(new_data)[1])
for(i in 1:dim(new_data)[2]) {
no_na = no_na & complete.cases(new_data[, i])
}
new_data = new_data[no_na, , drop=FALSE]
forest = randomForest(formula, new_data,
ntree = 1000, keep.forest = FALSE, importance = TRUE)
res = as.data.frame(importance(forest, type = importance.type))
colnames(res)[1] = "attr_importance"
return(res)
}
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