This post mentions that Caret rpart is more accurate than rpart due to bootstrapping and cross validation:
Why do results using caret::train(..., method = "rpart") differ from rpart::rpart(...)?
Although when I compare both methods, I get an accuracy of 0.4879 for Caret rpart and 0.7347 for rpart (I have copied my code below).
Besides that the classificationtree for Caret rpart has only a few nodes (splits) compared to rpart
Does anyone understand these differences?
Thank you!
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Loading libraries and the data
This is an R Markdown document. First we load the libraries and the data and split the trainingdata into a training and a testset.
```{r section1, echo=TRUE}
# load libraries
library(knitr)
library(caret)
suppressMessages(library(rattle))
library(rpart.plot)
# set the URL for the download
wwwTrain <- "http://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"
wwwTest <- "http://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"
# download the datasets
training <- read.csv(url(wwwTrain))
testing <- read.csv(url(wwwTest))
# create a partition with the training dataset
inTrain <- createDataPartition(training$classe, p=0.05, list=FALSE)
TrainSet <- training[inTrain, ]
TestSet <- training[-inTrain, ]
dim(TrainSet)
# set seed for reproducibility
set.seed(12345)
```
## Cleaning the data
```{r section2, echo=TRUE}
# remove variables with Nearly Zero Variance
NZV <- nearZeroVar(TrainSet)
TrainSet <- TrainSet[, -NZV]
TestSet <- TestSet[, -NZV]
dim(TrainSet)
dim(TestSet)
# remove variables that are mostly NA
AllNA <- sapply(TrainSet, function(x) mean(is.na(x))) > 0.95
TrainSet <- TrainSet[, AllNA==FALSE]
TestSet <- TestSet[, AllNA==FALSE]
dim(TrainSet)
dim(TestSet)
# remove identification only variables (columns 1 to 5)
TrainSet <- TrainSet[, -(1:5)]
TestSet <- TestSet[, -(1:5)]
dim(TrainSet)
```
## Prediction modelling
First we build a classification model using Caret with the rpart method:
```{r section4, echo=TRUE}
mod_rpart <- train(classe ~ ., method = "rpart", data = TrainSet)
pred_rpart <- predict(mod_rpart, TestSet)
confusionMatrix(pred_rpart, TestSet$classe)
mod_rpart$finalModel
fancyRpartPlot(mod_rpart$finalModel)
```
Second we build a similar model using rpart:
```{r section7, echo=TRUE}
# model fit
set.seed(12345)
modFitDecTree <- rpart(classe ~ ., data=TrainSet, method="class")
fancyRpartPlot(modFitDecTree)
# prediction on Test dataset
predictDecTree <- predict(modFitDecTree, newdata=TestSet, type="class")
confMatDecTree <- confusionMatrix(predictDecTree, TestSet$classe)
confMatDecTree
```
