Mutiple Machine Learning Algorithms
Xiangzhu Long
Background
Using devices such as Jawbone Up, Nike FuelBand, and Fitbit it is now possible to collect a large amount of data about personal activity relatively inexpensively. These type of devices are part of the quantified self movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. One thing that people regularly do is quantify how much of a particular activity they do, but they rarely quantify how well they do it. In this project, your goal will be to use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants. They were asked to perform barbell lifts correctly and incorrectly in 5 different ways. More information is available from the website (see the section on the Weight Lifting Exercise Dataset).
Preprocessing Data
trainingset <- read.csv("training.csv", na.strings=c("NA","#DIV/0!", ""))
testingset <- read.csv('testing.csv', na.strings=c("NA","#DIV/0!", ""))
# Delete columns with all missing values
trainingset<-trainingset[,colSums(is.na(trainingset)) == 0]
testingset <-testingset[,colSums(is.na(testingset)) == 0]
library(caret)## Loading required package: lattice
## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 3.1.3subsamples <- createDataPartition(y=trainingset$classe, p=0.75, list=FALSE)
subTraining <- trainingset[subsamples, ]
subTesting <- trainingset[-subsamples, ]
plot(subTraining$classe, col="blue", main="5 Levels of the variable class of the subTraining dataset", xlab="class levels", ylab="Frequency")
library(rpart)
model1 <- rpart(classe ~ ., data=subTraining, method="class")
# Predicting:
prediction1 <- predict(model1, subTesting, type = "class")
confusionMatrix(prediction1, subTesting$classe)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1395 0 0 0 0
## B 0 948 0 0 0
## C 0 1 855 0 0
## D 0 0 0 804 0
## E 0 0 0 0 901
##
## Overall Statistics
##
## Accuracy : 0.9998
## 95% CI : (0.9989, 1)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9997
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 1.0000 0.9989 1.0000 1.0000 1.0000
## Specificity 1.0000 1.0000 0.9998 1.0000 1.0000
## Pos Pred Value 1.0000 1.0000 0.9988 1.0000 1.0000
## Neg Pred Value 1.0000 0.9997 1.0000 1.0000 1.0000
## Prevalence 0.2845 0.1935 0.1743 0.1639 0.1837
## Detection Rate 0.2845 0.1933 0.1743 0.1639 0.1837
## Detection Prevalence 0.2845 0.1933 0.1746 0.1639 0.1837
## Balanced Accuracy 1.0000 0.9995 0.9999 1.0000 1.0000library(randomForest)## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.model2 <- randomForest(classe ~. , data=subTraining, method="class")
# Predicting:
prediction2 <- predict(model2, subTesting, type = "class")
confusionMatrix(prediction2, subTesting$classe)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1395 0 0 0 0
## B 0 949 0 0 0
## C 0 0 855 0 0
## D 0 0 0 804 0
## E 0 0 0 0 901
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9992, 1)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 1.0000 1.0000 1.0000 1.0000 1.0000
## Specificity 1.0000 1.0000 1.0000 1.0000 1.0000
## Pos Pred Value 1.0000 1.0000 1.0000 1.0000 1.0000
## Neg Pred Value 1.0000 1.0000 1.0000 1.0000 1.0000
## Prevalence 0.2845 0.1935 0.1743 0.1639 0.1837
## Detection Rate 0.2845 0.1935 0.1743 0.1639 0.1837
## Detection Prevalence 0.2845 0.1935 0.1743 0.1639 0.1837
## Balanced Accuracy 1.0000 1.0000 1.0000 1.0000 1.0000Random Forest algorithm performed better than Decision Trees.