Machine Learning
Nikos Kouvoutsakis
25-7-2018
Intro
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, our 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.
Goal
The goal of this project is to predict the manner in which they did the exercise. This is the “classe” variable in the training set. We use any of the other variables to predict with. We wll create a report describing how we built our model, how we used cross validation, what we think the expected out of sample error is, and why we made the choices we did. We will also use our prediction model to predict 20 different test cases.
Load needed packages and set defaults
knitr::opts_chunk$set(echo=TRUE)
knitr::opts_knit$set(root.dir = "~/R_Programs")
library(GGally)
library(dplyr)
library(ggplot2)
library(gridExtra)
library(grid)
library(kableExtra)
library(knitr)
library(caret)
library(rattle)
library(rpart)
library(rpart.plot)
library(RColorBrewer)
library(rattle)
library(randomForest)
library(doParallel)
library(lda)Create Project’s folder in working directory
if(!file.exists("./Machine.learning")){
dir.create("./Machine.learning")
}Data Processing
1.Loading project’s data
Download files if not exist and Load data
if(!file.exists("./Machine.learning/TrainingData.csv")){
fileUrl <- "https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"
download.file(fileUrl, destfile = "./Machine.learning/TrainingData.csv")
TrainingData<-read.csv("./Machine.learning/TrainingData.csv")
}else{
TrainingData<-read.csv("./Machine.learning/TrainingData.csv")
}
if(!file.exists("./Machine.learning/ValidationData.csv")){
fileUrl <- "https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"
download.file(fileUrl, destfile = "./Machine.learning/ValidationData.csv")
ValidationData<-read.csv("./Machine.learning/ValidationData.csv")
}else{
ValidationData<-read.csv("./Machine.learning/ValidationData.csv")
}
str(TrainingData)## 'data.frame': 19622 obs. of 160 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ user_name : Factor w/ 6 levels "adelmo","carlitos",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ raw_timestamp_part_1 : int 1323084231 1323084231 1323084231 1323084232 1323084232 1323084232 1323084232 1323084232 1323084232 1323084232 ...
## $ raw_timestamp_part_2 : int 788290 808298 820366 120339 196328 304277 368296 440390 484323 484434 ...
## $ cvtd_timestamp : Factor w/ 20 levels "02/12/2011 13:32",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ new_window : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ num_window : int 11 11 11 12 12 12 12 12 12 12 ...
## $ roll_belt : num 1.41 1.41 1.42 1.48 1.48 1.45 1.42 1.42 1.43 1.45 ...
## $ pitch_belt : num 8.07 8.07 8.07 8.05 8.07 8.06 8.09 8.13 8.16 8.17 ...
## $ yaw_belt : num -94.4 -94.4 -94.4 -94.4 -94.4 -94.4 -94.4 -94.4 -94.4 -94.4 ...
## $ total_accel_belt : int 3 3 3 3 3 3 3 3 3 3 ...
## $ kurtosis_roll_belt : Factor w/ 397 levels "","-0.016850",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_picth_belt : Factor w/ 317 levels "","-0.021887",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_yaw_belt : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_belt : Factor w/ 395 levels "","-0.003095",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_belt.1 : Factor w/ 338 levels "","-0.005928",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_yaw_belt : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ max_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ max_picth_belt : int NA NA NA NA NA NA NA NA NA NA ...
## $ max_yaw_belt : Factor w/ 68 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ min_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ min_pitch_belt : int NA NA NA NA NA NA NA NA NA NA ...
## $ min_yaw_belt : Factor w/ 68 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ amplitude_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ amplitude_pitch_belt : int NA NA NA NA NA NA NA NA NA NA ...
## $ amplitude_yaw_belt : Factor w/ 4 levels "","#DIV/0!","0.00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ var_total_accel_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_pitch_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_pitch_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_pitch_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_yaw_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_yaw_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_yaw_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ gyros_belt_x : num 0 0.02 0 0.02 0.02 0.02 0.02 0.02 0.02 0.03 ...
## $ gyros_belt_y : num 0 0 0 0 0.02 0 0 0 0 0 ...
## $ gyros_belt_z : num -0.02 -0.02 -0.02 -0.03 -0.02 -0.02 -0.02 -0.02 -0.02 0 ...
## $ accel_belt_x : int -21 -22 -20 -22 -21 -21 -22 -22 -20 -21 ...
## $ accel_belt_y : int 4 4 5 3 2 4 3 4 2 4 ...
## $ accel_belt_z : int 22 22 23 21 24 21 21 21 24 22 ...
## $ magnet_belt_x : int -3 -7 -2 -6 -6 0 -4 -2 1 -3 ...
## $ magnet_belt_y : int 599 608 600 604 600 603 599 603 602 609 ...
## $ magnet_belt_z : int -313 -311 -305 -310 -302 -312 -311 -313 -312 -308 ...
## $ roll_arm : num -128 -128 -128 -128 -128 -128 -128 -128 -128 -128 ...
## $ pitch_arm : num 22.5 22.5 22.5 22.1 22.1 22 21.9 21.8 21.7 21.6 ...
## $ yaw_arm : num -161 -161 -161 -161 -161 -161 -161 -161 -161 -161 ...
## $ total_accel_arm : int 34 34 34 34 34 34 34 34 34 34 ...
## $ var_accel_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_pitch_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_pitch_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_pitch_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_yaw_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_yaw_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_yaw_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ gyros_arm_x : num 0 0.02 0.02 0.02 0 0.02 0 0.02 0.02 0.02 ...
## $ gyros_arm_y : num 0 -0.02 -0.02 -0.03 -0.03 -0.03 -0.03 -0.02 -0.03 -0.03 ...
## $ gyros_arm_z : num -0.02 -0.02 -0.02 0.02 0 0 0 0 -0.02 -0.02 ...
## $ accel_arm_x : int -288 -290 -289 -289 -289 -289 -289 -289 -288 -288 ...
## $ accel_arm_y : int 109 110 110 111 111 111 111 111 109 110 ...
## $ accel_arm_z : int -123 -125 -126 -123 -123 -122 -125 -124 -122 -124 ...
## $ magnet_arm_x : int -368 -369 -368 -372 -374 -369 -373 -372 -369 -376 ...
## $ magnet_arm_y : int 337 337 344 344 337 342 336 338 341 334 ...
## $ magnet_arm_z : int 516 513 513 512 506 513 509 510 518 516 ...
## $ kurtosis_roll_arm : Factor w/ 330 levels "","-0.02438",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_picth_arm : Factor w/ 328 levels "","-0.00484",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_yaw_arm : Factor w/ 395 levels "","-0.01548",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_arm : Factor w/ 331 levels "","-0.00051",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_pitch_arm : Factor w/ 328 levels "","-0.00184",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_yaw_arm : Factor w/ 395 levels "","-0.00311",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ max_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ max_picth_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ max_yaw_arm : int NA NA NA NA NA NA NA NA NA NA ...
## $ min_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ min_pitch_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ min_yaw_arm : int NA NA NA NA NA NA NA NA NA NA ...
## $ amplitude_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ amplitude_pitch_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ amplitude_yaw_arm : int NA NA NA NA NA NA NA NA NA NA ...
## $ roll_dumbbell : num 13.1 13.1 12.9 13.4 13.4 ...
## $ pitch_dumbbell : num -70.5 -70.6 -70.3 -70.4 -70.4 ...
## $ yaw_dumbbell : num -84.9 -84.7 -85.1 -84.9 -84.9 ...
## $ kurtosis_roll_dumbbell : Factor w/ 398 levels "","-0.0035","-0.0073",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_picth_dumbbell : Factor w/ 401 levels "","-0.0163","-0.0233",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_yaw_dumbbell : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_dumbbell : Factor w/ 401 levels "","-0.0082","-0.0096",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_pitch_dumbbell : Factor w/ 402 levels "","-0.0053","-0.0084",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_yaw_dumbbell : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ max_roll_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## $ max_picth_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## $ max_yaw_dumbbell : Factor w/ 73 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ min_roll_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## $ min_pitch_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## $ min_yaw_dumbbell : Factor w/ 73 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ amplitude_roll_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## [list output truncated]str(ValidationData)## 'data.frame': 20 obs. of 160 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ user_name : Factor w/ 6 levels "adelmo","carlitos",..: 6 5 5 1 4 5 5 5 2 3 ...
## $ raw_timestamp_part_1 : int 1323095002 1322673067 1322673075 1322832789 1322489635 1322673149 1322673128 1322673076 1323084240 1322837822 ...
## $ raw_timestamp_part_2 : int 868349 778725 342967 560311 814776 510661 766645 54671 916313 384285 ...
## $ cvtd_timestamp : Factor w/ 11 levels "02/12/2011 13:33",..: 5 10 10 1 6 11 11 10 3 2 ...
## $ new_window : Factor w/ 1 level "no": 1 1 1 1 1 1 1 1 1 1 ...
## $ num_window : int 74 431 439 194 235 504 485 440 323 664 ...
## $ roll_belt : num 123 1.02 0.87 125 1.35 -5.92 1.2 0.43 0.93 114 ...
## $ pitch_belt : num 27 4.87 1.82 -41.6 3.33 1.59 4.44 4.15 6.72 22.4 ...
## $ yaw_belt : num -4.75 -88.9 -88.5 162 -88.6 -87.7 -87.3 -88.5 -93.7 -13.1 ...
## $ total_accel_belt : int 20 4 5 17 3 4 4 4 4 18 ...
## $ kurtosis_roll_belt : logi NA NA NA NA NA NA ...
## $ kurtosis_picth_belt : logi NA NA NA NA NA NA ...
## $ kurtosis_yaw_belt : logi NA NA NA NA NA NA ...
## $ skewness_roll_belt : logi NA NA NA NA NA NA ...
## $ skewness_roll_belt.1 : logi NA NA NA NA NA NA ...
## $ skewness_yaw_belt : logi NA NA NA NA NA NA ...
## $ max_roll_belt : logi NA NA NA NA NA NA ...
## $ max_picth_belt : logi NA NA NA NA NA NA ...
## $ max_yaw_belt : logi NA NA NA NA NA NA ...
## $ min_roll_belt : logi NA NA NA NA NA NA ...
## $ min_pitch_belt : logi NA NA NA NA NA NA ...
## $ min_yaw_belt : logi NA NA NA NA NA NA ...
## $ amplitude_roll_belt : logi NA NA NA NA NA NA ...
## $ amplitude_pitch_belt : logi NA NA NA NA NA NA ...
## $ amplitude_yaw_belt : logi NA NA NA NA NA NA ...
## $ var_total_accel_belt : logi NA NA NA NA NA NA ...
## $ avg_roll_belt : logi NA NA NA NA NA NA ...
## $ stddev_roll_belt : logi NA NA NA NA NA NA ...
## $ var_roll_belt : logi NA NA NA NA NA NA ...
## $ avg_pitch_belt : logi NA NA NA NA NA NA ...
## $ stddev_pitch_belt : logi NA NA NA NA NA NA ...
## $ var_pitch_belt : logi NA NA NA NA NA NA ...
## $ avg_yaw_belt : logi NA NA NA NA NA NA ...
## $ stddev_yaw_belt : logi NA NA NA NA NA NA ...
## $ var_yaw_belt : logi NA NA NA NA NA NA ...
## $ gyros_belt_x : num -0.5 -0.06 0.05 0.11 0.03 0.1 -0.06 -0.18 0.1 0.14 ...
## $ gyros_belt_y : num -0.02 -0.02 0.02 0.11 0.02 0.05 0 -0.02 0 0.11 ...
## $ gyros_belt_z : num -0.46 -0.07 0.03 -0.16 0 -0.13 0 -0.03 -0.02 -0.16 ...
## $ accel_belt_x : int -38 -13 1 46 -8 -11 -14 -10 -15 -25 ...
## $ accel_belt_y : int 69 11 -1 45 4 -16 2 -2 1 63 ...
## $ accel_belt_z : int -179 39 49 -156 27 38 35 42 32 -158 ...
## $ magnet_belt_x : int -13 43 29 169 33 31 50 39 -6 10 ...
## $ magnet_belt_y : int 581 636 631 608 566 638 622 635 600 601 ...
## $ magnet_belt_z : int -382 -309 -312 -304 -418 -291 -315 -305 -302 -330 ...
## $ roll_arm : num 40.7 0 0 -109 76.1 0 0 0 -137 -82.4 ...
## $ pitch_arm : num -27.8 0 0 55 2.76 0 0 0 11.2 -63.8 ...
## $ yaw_arm : num 178 0 0 -142 102 0 0 0 -167 -75.3 ...
## $ total_accel_arm : int 10 38 44 25 29 14 15 22 34 32 ...
## $ var_accel_arm : logi NA NA NA NA NA NA ...
## $ avg_roll_arm : logi NA NA NA NA NA NA ...
## $ stddev_roll_arm : logi NA NA NA NA NA NA ...
## $ var_roll_arm : logi NA NA NA NA NA NA ...
## $ avg_pitch_arm : logi NA NA NA NA NA NA ...
## $ stddev_pitch_arm : logi NA NA NA NA NA NA ...
## $ var_pitch_arm : logi NA NA NA NA NA NA ...
## $ avg_yaw_arm : logi NA NA NA NA NA NA ...
## $ stddev_yaw_arm : logi NA NA NA NA NA NA ...
## $ var_yaw_arm : logi NA NA NA NA NA NA ...
## $ gyros_arm_x : num -1.65 -1.17 2.1 0.22 -1.96 0.02 2.36 -3.71 0.03 0.26 ...
## $ gyros_arm_y : num 0.48 0.85 -1.36 -0.51 0.79 0.05 -1.01 1.85 -0.02 -0.5 ...
## $ gyros_arm_z : num -0.18 -0.43 1.13 0.92 -0.54 -0.07 0.89 -0.69 -0.02 0.79 ...
## $ accel_arm_x : int 16 -290 -341 -238 -197 -26 99 -98 -287 -301 ...
## $ accel_arm_y : int 38 215 245 -57 200 130 79 175 111 -42 ...
## $ accel_arm_z : int 93 -90 -87 6 -30 -19 -67 -78 -122 -80 ...
## $ magnet_arm_x : int -326 -325 -264 -173 -170 396 702 535 -367 -420 ...
## $ magnet_arm_y : int 385 447 474 257 275 176 15 215 335 294 ...
## $ magnet_arm_z : int 481 434 413 633 617 516 217 385 520 493 ...
## $ kurtosis_roll_arm : logi NA NA NA NA NA NA ...
## $ kurtosis_picth_arm : logi NA NA NA NA NA NA ...
## $ kurtosis_yaw_arm : logi NA NA NA NA NA NA ...
## $ skewness_roll_arm : logi NA NA NA NA NA NA ...
## $ skewness_pitch_arm : logi NA NA NA NA NA NA ...
## $ skewness_yaw_arm : logi NA NA NA NA NA NA ...
## $ max_roll_arm : logi NA NA NA NA NA NA ...
## $ max_picth_arm : logi NA NA NA NA NA NA ...
## $ max_yaw_arm : logi NA NA NA NA NA NA ...
## $ min_roll_arm : logi NA NA NA NA NA NA ...
## $ min_pitch_arm : logi NA NA NA NA NA NA ...
## $ min_yaw_arm : logi NA NA NA NA NA NA ...
## $ amplitude_roll_arm : logi NA NA NA NA NA NA ...
## $ amplitude_pitch_arm : logi NA NA NA NA NA NA ...
## $ amplitude_yaw_arm : logi NA NA NA NA NA NA ...
## $ roll_dumbbell : num -17.7 54.5 57.1 43.1 -101.4 ...
## $ pitch_dumbbell : num 25 -53.7 -51.4 -30 -53.4 ...
## $ yaw_dumbbell : num 126.2 -75.5 -75.2 -103.3 -14.2 ...
## $ kurtosis_roll_dumbbell : logi NA NA NA NA NA NA ...
## $ kurtosis_picth_dumbbell : logi NA NA NA NA NA NA ...
## $ kurtosis_yaw_dumbbell : logi NA NA NA NA NA NA ...
## $ skewness_roll_dumbbell : logi NA NA NA NA NA NA ...
## $ skewness_pitch_dumbbell : logi NA NA NA NA NA NA ...
## $ skewness_yaw_dumbbell : logi NA NA NA NA NA NA ...
## $ max_roll_dumbbell : logi NA NA NA NA NA NA ...
## $ max_picth_dumbbell : logi NA NA NA NA NA NA ...
## $ max_yaw_dumbbell : logi NA NA NA NA NA NA ...
## $ min_roll_dumbbell : logi NA NA NA NA NA NA ...
## $ min_pitch_dumbbell : logi NA NA NA NA NA NA ...
## $ min_yaw_dumbbell : logi NA NA NA NA NA NA ...
## $ amplitude_roll_dumbbell : logi NA NA NA NA NA NA ...
## [list output truncated]2.Data cleaning and preprocess
- Remove first 7 variables since they only provide us with information on persons which carried the tests.
- Removing Columns with more than 80% of NAs or Blank values.
- Removing nearZero variables
- Keep common Variables in both Training and Validation Datasets.
All above actions are applied in both Training and Validation datasets.
TrainingData<-TrainingData[,-c(1:7)]
TrainingData<-TrainingData[, -which(colSums(is.na(TrainingData) | TrainingData=="") > 0.8*nrow(TrainingData))]
ValidationData<-ValidationData[,-c(1:7)]
ValidationData<-ValidationData[, -which(colSums(is.na(ValidationData) | ValidationData=="") > 0.8*nrow(ValidationData))]
nzvTraining<-nearZeroVar(TrainingData,saveMetrics = TRUE)
nzvValidation<-nearZeroVar(ValidationData,saveMetrics = TRUE)
TrainingData<-TrainingData[,nzvTraining$nzv==FALSE]
ValidationData<-ValidationData[,nzvValidation$nzv==FALSE]
TrainingDataColum<-colnames(TrainingData)
ValidationDataColum<-colnames(ValidationData)
Allcolnames<-c(TrainingDataColum,ValidationDataColum)
Allcolnames<-data.frame(Allcolnames)
colnames(Allcolnames)<-c("col")
Allcolnames2<-as.data.frame(table(Allcolnames$col))
output<-Allcolnames2[Allcolnames2$Freq==2,]
unifcol<-as.vector(output$Var1)
TrainingDataUNI<-TrainingData[,unifcol]
TrainingDataUNI$classe<-TrainingData$classe
#str(TrainingDataUNI)
ValidationDataUNI<-ValidationData[,unifcol]
#str(ValidationDataUNI)3.Split cleaned training data into a new Train and Test sets, providing their respective dimensions.
set.seed(12345)
inTrain<-createDataPartition(y=TrainingDataUNI$classe,p=0.75,list=FALSE)
finalTrain<-TrainingDataUNI[inTrain,]
finalTest<-TrainingDataUNI[-inTrain,]
dim(finalTrain)## [1] 14718 53dim(finalTest)## [1] 4904 53Machine learning Algorithms
We will apply 4 differenct machine learning algorithms
- Classification Tree
- Linear Discriminant Analysis
- Random Forests
- Gradient Boosting method
and we will compare their respective results on the Train Set. (In Sample error) This will be a first indication on which model performs better.
As we know, in Sample error is always smaller than the out Sample error, so the error rates will be smaller than if we applied the models on the created Test set.
Algorithms application on Train Set
set.seed(54321)
TC<-trainControl(method="cv",number=5)
modFitForest<-train(classe~.,data=finalTrain,method="rpart",trControl=TC)
predRpart<-predict(modFitForest,finalTest)
Accuracy.Tree<-confusionMatrix(finalTest$classe,predRpart)set.seed(54321)
modFitLDA<-train(classe~.,data=finalTrain,method="lda",trControl=trainControl(method="cv",number=5))
predLDA<-predict(modFitLDA,finalTest)
Accuracy.LDA<-confusionMatrix(finalTest$classe,predLDA)set.seed(54321)
no_cores <- detectCores()
cl <- makeCluster(no_cores)
registerDoParallel(cl)
modFitRForest<-train(classe~.,data=finalTrain,method="rf",trControl=TC)
stopCluster(cl)
predRF<-predict(modFitRForest,finalTest)
Accuracy.RForests<-confusionMatrix(finalTest$classe,predRF)set.seed(54321)
no_cores <- detectCores()
cl <- makeCluster(no_cores)
registerDoParallel(cl)
modFitGBM<-train(classe~.,data=finalTrain,method="gbm",trControl=TC, verbose=FALSE)
stopCluster(cl)
predGBM<-predict(modFitGBM,finalTest)
Accuracy.GBM<-confusionMatrix(finalTest$classe,predGBM)Models comparison (In Sample error)
results <- resamples(list(Clas.Tree=modFitForest, LDA=modFitLDA, RForests=modFitRForest, GBM=modFitGBM ))
summary(results)[[3]][1]## $Accuracy
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## Clas.Tree 0.5628397 0.5923913 0.5944935 0.5906374 0.5987088 0.6047538 0
## LDA 0.6925951 0.7015280 0.7037037 0.7034259 0.7055027 0.7138001 0
## RForests 0.9911715 0.9925272 0.9928620 0.9925263 0.9928668 0.9932042 0
## GBM 0.9578804 0.9609242 0.9612903 0.9616119 0.9639701 0.9639946 0dotplot(results)
Algorithms application on Test set (Trees - Tables - Out Sample error)
rpart.plot(modFitForest$finalModel,roundint=FALSE)
plot(Accuracy.Tree$table)
Accuracy.Tree$table## Reference
## Prediction A B C D E
## A 870 159 273 88 5
## B 162 530 214 43 0
## C 29 36 674 116 0
## D 46 136 429 193 0
## E 16 221 224 51 389Accuracy.Tree$overall[1]## Accuracy
## 0.5415987plot(Accuracy.LDA$table)
Accuracy.LDA$table## Reference
## Prediction A B C D E
## A 1157 24 116 90 8
## B 158 597 116 41 37
## C 90 82 575 87 21
## D 37 52 106 573 36
## E 39 168 80 93 521Accuracy.LDA$overall[1]## Accuracy
## 0.6980016plot(Accuracy.RForests$table)
Accuracy.RForests$table## Reference
## Prediction A B C D E
## A 1394 1 0 0 0
## B 6 938 5 0 0
## C 0 3 849 3 0
## D 0 0 8 796 0
## E 0 0 2 5 894Accuracy.RForests$overall[1]## Accuracy
## 0.9932708plot(Accuracy.GBM$table)
Accuracy.GBM$table## Reference
## Prediction A B C D E
## A 1377 15 2 1 0
## B 37 885 26 0 1
## C 0 16 826 12 1
## D 2 2 24 772 4
## E 3 9 9 16 864Accuracy.GBM$overall[1]## Accuracy
## 0.9632953Course Project Prediction Quiz Portion
Random Forests was classified as the best model to use. We will predict all 20 cases in Validation set by applying Random Forests algorithm.
quiz<-predict(modFitRForest,newdata=ValidationDataUNI)
quiz## [1] B A B A A E D B A A B C B A E E A B B B
## Levels: A B C D E