Nothing
# ------------------- Prediccion Nuevos
#Elimina la información de newCases
borrar.datos <- function (newCases, prueba = FALSE){
if(!prueba){
newCases$originales <- NULL
newCases$datos.aprendizaje <- NULL
newCases$variable.predecir <- NULL
newCases$modelo <- NULL
newCases$m.seleccionado <- NULL
}
newCases$prediccion <- NULL
newCases$datos.prueba <- NULL
}
# Códigos de Modelos Ind.Nuevos--------------------------------------------------------------------------------------------------
# BAYES
bayes.modelo.np <- function(variable.pr = ""){
return(paste0("modelo.nuevos <<- train.bayes(",variable.pr,"~., data = datos.aprendizaje.completos)"))
}
# BOOSTING
boosting.modelo.np <- function(variable.pr = NULL, iter = 50, maxdepth = 1, minsplit = 1){
iter <- ifelse(!is.numeric(iter), 50, iter)
maxdepth <- ifelse(!is.numeric(maxdepth) && maxdepth > 30, 15, maxdepth)
minsplit <- ifelse(!is.numeric(minsplit), 1, minsplit)
codigo <- paste0("modelo.nuevos <<- train.adabag(",variable.pr,"~., data = datos.aprendizaje.completos, mfinal = ",iter,",
control = rpart.control(minsplit = ",minsplit,", maxdepth = ",maxdepth,"))")
return(codigo)
}
# DT
dt.modelo.np <- function(variable.pr = NULL, minsplit = 20, maxdepth = 15, split = "gini"){
minsplit <- ifelse(!is.numeric(minsplit), 1, minsplit )
maxdepth <- ifelse(!is.numeric(maxdepth) || maxdepth > 30, 15, maxdepth)
codigo <- paste0("modelo.nuevos <<- train.rpart(",variable.pr,"~., data = datos.aprendizaje.completos,
control = rpart.control(minsplit = ",minsplit,", maxdepth = ", maxdepth,"),parms = list(split = '",split,"'))")
return(codigo)
}
# KNN
kkn.modelo.np <- function(variable.pr = NULL, scale = TRUE,kmax = 7, kernel = "optimal"){
return(paste0("modelo.nuevos <<- traineR::train.knn(",variable.pr,"~., data = datos.aprendizaje.completos, scale =",scale,", kmax=",kmax,", kernel = '",kernel,"')"))
}
# RL
rl.modelo.np <- function(variable.pr = ""){
return(paste0("modelo.nuevos <<- traineR::train.glm(",variable.pr,"~., data = datos.aprendizaje.completos)"))
}
# NN
nn.modelo.np <- function(variable.pr = "",threshold = 0.01, stepmax = 1000, cant.cap = 2, ...){
capas <- as.string.c(as.numeric(list(...)[1:cant.cap]), .numeric = TRUE)
stepmax <- ifelse(1000>stepmax, 1000, stepmax)
threshold <- ifelse(0.01>threshold, 0.01, threshold)
return(paste0("modelo.nuevos <<- train.neuralnet(",variable.pr,"~., data = datos.aprendizaje.completos, hidden = ",capas,",\n\t\t\tlinear.output = FALSE,",
"threshold = ",threshold,", stepmax = ",stepmax,")\n"))
}
# RLR
rlr.modelo.np <- function(variable.pr = NULL, alpha = 0, escalar = TRUE){
return(paste0("modelo.nuevos <<- train.glmnet(",variable.pr,"~., data = datos.aprendizaje.completos, standardize = ",escalar,", alpha = ",alpha,", family = 'multinomial')"))
}
# RF
rf.modelo.np <- function(variable.pr = NULL, ntree = 500, mtry = 1){
ntree <- ifelse(!is.numeric(ntree), 500, ntree)
Codigo <- paste0("modelo.nuevos <<- train.randomForest(",variable.pr,"~., data = datos.aprendizaje.completos,importance = TRUE,",
" ntree =",ntree,",mtry =",mtry,")")
return(Codigo)
}
# SVM
svm.modelo.np <- function(variable.pr = NULL, scale = TRUE, kernel = "linear"){
return(paste0("modelo.nuevos <<- traineR::train.svm(",variable.pr,"~., data = datos.aprendizaje.completos, scale =",scale,", kernel = '",kernel,"')"))
}
# XGBOOSTING
xgb.modelo.np <- function(variable.pr = "", booster = "gbtree", max.depth = 6, n.rounds = 60){
return(paste0("modelo.nuevos <<- traineR::train.xgboost(",variable.pr,"~., data = datos.aprendizaje.completos, booster ='",booster,"', max_depth=",max.depth,", nrounds = ",n.rounds,")"))
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.