search for: impurity_r

...lt;- function(p) { if(any(p==1)) return(0) # works for the case when y has only 0 and 1 categories... -sum(p*log(p,2)) } gini <- function(p) {sum(p*(1-p))} obs_1 <- y[x>.5] obs_0 <- y[x<.5] n_l <- sum(x>.5) n_R <- sum(x<.5) n <- length(x) # for entropy (information) impurity_root <- entropy(prop.table(table(y))) impurity_l <- entropy(prop.table(table(obs_0))) impurity_R <-entropy(prop.table(table(obs_1))) # shouldn't this have been "improve" ?? impurity_root - ((n_l/n)*impurity_l + (n_R/n)*impurity_R) # 0.7272 # for "gini" impurity_root...

...ion is not as clear) # creating data set.seed(1324) y <- sample(c(0,1), 20, T) x <- y x[1:5] <- 0 # manually making the first split obs_L <- y[x<.5] obs_R <- y[x>.5] n_L <- sum(x<.5) n_R <- sum(x>.5) n <- length(x) calc.impurity <- function(func = gini) { impurity_root <- func(prop.table(table(y))) impurity_L <- func(prop.table(table(obs_L))) impurity_R <-func(prop.table(table(obs_R))) imp <- impurity_root - ((n_L/n)*impurity_l + (n_R/n)*impurity_R) # 0.3757 imp*n } # for "gini" require(rpart) fit <- rpart(y~x, method = "class&...