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library(caret)
## Warning: package 'caret' was built under R version 4.3.3
## Loading required package: ggplot2
## Loading required package: lattice
library(klaR)
## Warning: package 'klaR' was built under R version 4.3.3
## Loading required package: MASS
#load the data
data(iris)
#define an 80%/20% train/test split of the dataset
trainIndex<-createDataPartition(iris$Species, p=0.80, list=FALSE)
dataTrain<-iris[trainIndex,]
dataTest<-iris[-trainIndex,]
#train a ntaive Bayes model
fit<-NaiveBayes(Species~., data=dataTrain)
#make predictions
predictions<-predict(fit,dataTest[,1:4])
#summarize
confusionMatrix(predictions$class,dataTest$Species)
## Confusion Matrix and Statistics
## 
##             Reference
## Prediction   setosa versicolor virginica
##   setosa         10          0         0
##   versicolor      0          9         0
##   virginica       0          1        10
## 
## Overall Statistics
##                                           
##                Accuracy : 0.9667          
##                  95% CI : (0.8278, 0.9992)
##     No Information Rate : 0.3333          
##     P-Value [Acc > NIR] : 2.963e-13       
##                                           
##                   Kappa : 0.95            
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: setosa Class: versicolor Class: virginica
## Sensitivity                 1.0000            0.9000           1.0000
## Specificity                 1.0000            1.0000           0.9500
## Pos Pred Value              1.0000            1.0000           0.9091
## Neg Pred Value              1.0000            0.9524           1.0000
## Prevalence                  0.3333            0.3333           0.3333
## Detection Rate              0.3333            0.3000           0.3333
## Detection Prevalence        0.3333            0.3000           0.3667
## Balanced Accuracy           1.0000            0.9500           0.9750
#define training Control
trainControl<-trainControl(method="boot",  number=100)#using the bootstrap method 
#evaluate the model
fit<-train(Species~ .,data=iris,trControl=trainControl,method="nb")
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
print(fit)
## Naive Bayes 
## 
## 150 samples
##   4 predictor
##   3 classes: 'setosa', 'versicolor', 'virginica' 
## 
## No pre-processing
## Resampling: Bootstrapped (100 reps) 
## Summary of sample sizes: 150, 150, 150, 150, 150, 150, ... 
## Resampling results across tuning parameters:
## 
##   usekernel  Accuracy   Kappa    
##   FALSE      0.9522434  0.9276283
##    TRUE      0.9516945  0.9267730
## 
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
##  parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = FALSE and adjust
##  = 1.
library(caret)
#LOAD IRIS DATA SET
data(iris)
#define trainining control
trainControl<-trainControl(method="LOOCV")
#evaluate the model
fit<-train(Species~., data=iris, trControl=trainControl, method="nb")
print(fit)
## Naive Bayes 
## 
## 150 samples
##   4 predictor
##   3 classes: 'setosa', 'versicolor', 'virginica' 
## 
## No pre-processing
## Resampling: Leave-One-Out Cross-Validation 
## Summary of sample sizes: 149, 149, 149, 149, 149, 149, ... 
## Resampling results across tuning parameters:
## 
##   usekernel  Accuracy   Kappa
##   FALSE      0.9533333  0.93 
##    TRUE      0.9600000  0.94 
## 
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
##  parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = TRUE and adjust
##  = 1.
#K-fold cross validation methd
trainControl<-trainControl(method="CV", number=10)
#evaluate the model
fit<-train(Species~., data=iris, trControl=trainControl, method="nb")
print(fit)
## Naive Bayes 
## 
## 150 samples
##   4 predictor
##   3 classes: 'setosa', 'versicolor', 'virginica' 
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 135, 135, 135, 135, 135, 135, ... 
## Resampling results across tuning parameters:
## 
##   usekernel  Accuracy   Kappa
##   FALSE      0.9533333  0.93 
##    TRUE      0.9600000  0.94 
## 
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
##  parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = TRUE and adjust
##  = 1.
##### reopeated Cross Validation ####
trainControl<-trainControl(method="repeatedcv", number=10, repeats=3)
#evaluate the model
fit<-train(Species~., data=iris, trControl=trainControl, method="nb")
print(fit)
## Naive Bayes 
## 
## 150 samples
##   4 predictor
##   3 classes: 'setosa', 'versicolor', 'virginica' 
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 3 times) 
## Summary of sample sizes: 135, 135, 135, 135, 135, 135, ... 
## Resampling results across tuning parameters:
## 
##   usekernel  Accuracy   Kappa    
##   FALSE      0.9555556  0.9333333
##    TRUE      0.9555556  0.9333333
## 
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
##  parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = FALSE and adjust
##  = 1.