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t <- proc.time()# For manipulating the datasets
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(readr)
library(readxl)
# For plotting correlation matrix
library(ggcorrplot)## Loading required package: ggplot2# Machine Learning library
library(caret)## Loading required package: latticelibrary(catboost)
# For Multi-core processing support
library(parallel)
library(doParallel)## Loading required package: foreach## Loading required package: iteratorscl <- makePSOCKcluster(2)
registerDoParallel(cl)#Numerical dataset
dataset_num <- read_excel("rice.xlsx")
#Categorical dataset
dataset_cat <- read.csv("mushrooms.csv")
#Mix dataset
dataset_mix <- read_excel("bank.xlsx")dataset_cat %>% group_by(VEIL.TYPE) %>% summarise(total=n())## `summarise()` ungrouping output (override with `.groups` argument)#Eliminate VEIL.TYPE since it only has one value
dataset_cat <- dataset_cat %>% select(-VEIL.TYPE)
dataset_cat %>% group_by(STALK.ROOT) %>% summarise(total=n())## `summarise()` ungrouping output (override with `.groups` argument)#Eliminate STALK.ROOT since it has missing values
dataset_cat <- dataset_cat %>% select(-STALK.ROOT)dataset_num$CLASS <- as.factor(dataset_num$CLASS)
dataset_cat <- mutate_if(dataset_cat, is.character, as.factor)
dataset_mix <- mutate_if(dataset_mix, is.character, as.factor)#CATBOOST
catboost_function <- function(dataset){
#Split train and test
trainIndex <- createDataPartition(dataset$CLASS, p=0.80, list=FALSE)
data_train <- dataset[ trainIndex,]
data_test <- dataset[-trainIndex,]
#Train the model
fitControl <- trainControl(method="repeatedcv",
repeats = 2,
number = 5,
returnResamp = 'final',
savePredictions = 'final',
verboseIter = T,
allowParallel = T)
catboost_model <- train(
x = data_train[,!(names(data_train) %in% c("CLASS"))],
y = data_train$CLASS,
method = catboost.caret,
trControl = fitControl)
catboost_model
#Predict results
catboost_predictions=predict(catboost_model,data_test)
confusionMatrix(catboost_predictions,as.factor(data_test$CLASS))
}#RANDOM FOREST
rf_function <- function(dataset){
#Split train and test
trainIndex <- createDataPartition(dataset$CLASS, p=0.80, list=FALSE)
data_train <- dataset[ trainIndex,]
data_test <- dataset[-trainIndex,]
#Train the model
fitControl <- trainControl(method="repeatedcv",
repeats = 2,
number = 5,
returnResamp = 'final',
savePredictions = 'final',
verboseIter = T,
allowParallel = T)
train_formula<-formula(CLASS~.)
rf_model <- train(train_formula,
data = data_train,
method = "rf",
trControl = fitControl)
rf_model
#Predict results
rf_predictions=predict(rf_model,data_test)
confusionMatrix(rf_predictions,as.factor(data_test$CLASS))
}#EXECUTE CATBOOST AND RANDOMFOREST IN EACH DATASET
##Numerical Dataset
#Start time
t1 <- proc.time()
catboost_function(dataset_num)## Warning: The `i` argument of ``[`()` can't be a matrix as of tibble 3.0.0.
## Convert to a vector.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.## Aggregating results
## Selecting tuning parameters
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## 99: learn: 0.1640056 total: 476ms remaining: 0us## Confusion Matrix and Statistics
##
## Reference
## Prediction Cammeo Osmancik
## Cammeo 303 26
## Osmancik 23 410
##
## Accuracy : 0.9357
## 95% CI : (0.9159, 0.9521)
## No Information Rate : 0.5722
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.8688
##
## Mcnemar's Test P-Value : 0.7751
##
## Sensitivity : 0.9294
## Specificity : 0.9404
## Pos Pred Value : 0.9210
## Neg Pred Value : 0.9469
## Prevalence : 0.4278
## Detection Rate : 0.3976
## Detection Prevalence : 0.4318
## Balanced Accuracy : 0.9349
##
## 'Positive' Class : Cammeo
## #Stop time
proc.time()-t1## user system elapsed
## 2.02 0.18 56.50#Start time
t1 <- proc.time()
rf_function(dataset_num)## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 2 on full training set## Confusion Matrix and Statistics
##
## Reference
## Prediction Cammeo Osmancik
## Cammeo 300 29
## Osmancik 26 407
##
## Accuracy : 0.9278
## 95% CI : (0.9071, 0.9452)
## No Information Rate : 0.5722
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.8527
##
## Mcnemar's Test P-Value : 0.7874
##
## Sensitivity : 0.9202
## Specificity : 0.9335
## Pos Pred Value : 0.9119
## Neg Pred Value : 0.9400
## Prevalence : 0.4278
## Detection Rate : 0.3937
## Detection Prevalence : 0.4318
## Balanced Accuracy : 0.9269
##
## 'Positive' Class : Cammeo
## #Stop time
proc.time()-t1## user system elapsed
## 2.10 0.05 21.52##Categorical dataset
#Start time
t1 <- proc.time()
catboost_function(dataset_cat)## Aggregating results
## Selecting tuning parameters
## Fitting depth = 4, learning_rate = 0.135, iterations = 100, l2_leaf_reg = 1e-06, rsm = 0.9, border_count = 255 on full training set
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##
## Reference
## Prediction e p
## e 841 0
## p 0 783
##
## Accuracy : 1
## 95% CI : (0.9977, 1)
## No Information Rate : 0.5179
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.0000
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 1.0000
## Prevalence : 0.5179
## Detection Rate : 0.5179
## Detection Prevalence : 0.5179
## Balanced Accuracy : 1.0000
##
## 'Positive' Class : e
## #Stop time
proc.time()-t1## user system elapsed
## 3.44 0.29 89.06#Start time
t1 <- proc.time()
rf_function(dataset_cat)## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 46 on full training set## Confusion Matrix and Statistics
##
## Reference
## Prediction e p
## e 841 0
## p 0 783
##
## Accuracy : 1
## 95% CI : (0.9977, 1)
## No Information Rate : 0.5179
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.0000
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 1.0000
## Prevalence : 0.5179
## Detection Rate : 0.5179
## Detection Prevalence : 0.5179
## Balanced Accuracy : 1.0000
##
## 'Positive' Class : e
## #Stop time
proc.time()-t1## user system elapsed
## 16.79 0.15 268.00##Mix dataset
#Start time
t1 <- proc.time()
catboost_function(dataset_mix)## Aggregating results
## Selecting tuning parameters
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##
## Reference
## Prediction no yes
## no 777 73
## yes 23 31
##
## Accuracy : 0.8938
## 95% CI : (0.8719, 0.9131)
## No Information Rate : 0.885
## P-Value [Acc > NIR] : 0.2187
##
## Kappa : 0.3405
##
## Mcnemar's Test P-Value : 5.702e-07
##
## Sensitivity : 0.9712
## Specificity : 0.2981
## Pos Pred Value : 0.9141
## Neg Pred Value : 0.5741
## Prevalence : 0.8850
## Detection Rate : 0.8595
## Detection Prevalence : 0.9403
## Balanced Accuracy : 0.6347
##
## 'Positive' Class : no
## #Stop time
proc.time()-t1## user system elapsed
## 2.14 0.20 55.35#Start time
t1 <- proc.time()
rf_function(dataset_mix)## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 42 on full training set## Confusion Matrix and Statistics
##
## Reference
## Prediction no yes
## no 763 61
## yes 37 43
##
## Accuracy : 0.8916
## 95% CI : (0.8695, 0.9111)
## No Information Rate : 0.885
## P-Value [Acc > NIR] : 0.28629
##
## Kappa : 0.4082
##
## Mcnemar's Test P-Value : 0.02016
##
## Sensitivity : 0.9537
## Specificity : 0.4135
## Pos Pred Value : 0.9260
## Neg Pred Value : 0.5375
## Prevalence : 0.8850
## Detection Rate : 0.8440
## Detection Prevalence : 0.9115
## Balanced Accuracy : 0.6836
##
## 'Positive' Class : no
## #Stop time
proc.time()-t1## user system elapsed
## 10.01 0.11 131.03stopCluster(cl)proc.time()-t## user system elapsed
## 39.14 1.42 624.94