#' LM cv
#'
#' fonisce le previsioni della cv, da utilizzare dentro sapply(1:k,...)
#'
#' @param i i-esimo fold
#' @param formula formula del modello
#' @param folds prodotto da kfold function
#' @param y vettore. variabile risposta
#' @param x matrice variabili esplicative
#' @param kernel
#' @param cost
#' @param gamma
#' @param degree
#'
#' @export
svm_crossvalidation<-function(i,formula=NULL,y,x,folds,kernel='linear',cost,gamma=1,degree=3){
if(class(formula)=='NULL'){
m<- svm(x=x[-folds[[i]],],y=y[-folds[[i]]],kernel=kernel,gamma=gamma ,degree=degree,cost=cost,scale=F,probability = T)
}
else
{
formula<-as.formula(formula)
m<- svm(formula, data = x[-folds[[i]],],kernel=kernel,gamma=gamma ,degree=degree,cost=cost,scale=F,probability =T)
}
pred <- predict(m, newdata = x[folds[[i]],],probability = T)
return(attr(pred, "probabilities")[,1])
}
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