In sungcheolkim78/FiDEL: Fermi-Dirac based Ensemble Learning

ML training (Sonar)

Preparations

library(mlbench)
library(FiDEL)
library(caret)
library(tidyverse)

set.seed(1024)
data(Sonar)
inTraining0 <- createDataPartition(Sonar$Class, p = .75, list = FALSE)
training <- Sonar[ inTraining0,]
testing  <- Sonar[-inTraining0,]
testingY <- to_label(Sonar[-inTraining0, ncol(Sonar)])

table(Sonar[,ncol(Sonar)])

model_list <- c('nnet', 'rda', 'svmLinear', 'svmRadial', 'pls', 'knn', 'earth', 'avNNet', 'mlp', 'nb', 'rf', 'rpart', 'ctree', 'C5.0', 'gbm', 'bayesglm', 'glm', 'glmnet', 'simpls')
t1 <- mtrainer(model_list, dataInfo = 'Sonar')

t1 <- train(t1, Class~., training, update=F)

t1 <- t1 %>%
  addmodel.mtrainer(c('svmLinear', 'svmRadial', 'pls', 'knn', 'earth', 'avNNet')) %>%
  train(Class~., training, update=F)

plot(t1)

t1 <- predict(t1, newdata=testing)

#auclist <- apply(t1$predictions, 2, auc.rank, testingY)

fde1 <- fde(t1$predictions)
fde1 <- calculate_performance(fde1, testingY, alpha=7)

plot_cor(fde1, class_flag = 'positive')

fde1 <- fde(t1$predictions, testingY)

plot_performance(fde1, nsample=100, trendline=T)

plot_performance_nmethods(fde1, nmethod_list = 3:10, nsample=100)

plot_single(fde1, 'score')

store.mtrainer(t1, 'sonar_m8_pre.RData')
saveRDS(testingY, 'sonar_m8_y.RData')

sungcheolkim78/FiDEL documentation built on Nov. 13, 2024, 7:58 a.m.