download data
if(!file.exists("pml-training.csv")){download.file("https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv", destfile = "pml-training.csv")}
if(!file.exists("pml-testing.csv")){download.file("https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv", destfile = "pml-testing.csv")}
Load the necessary packages that will be used
library(caret, warn.conflicts = FALSE, quietly = TRUE )
## read data into r and replace missing values with NAs
train_data <- read.csv("pml-training.csv", na.strings = c("", "NA"))
test_data <- read.csv("pml-testing.csv", na.strings = c("", "NA"))
dim(train_data); dim(test_data)
## [1] 19622 160
## [1] 20 160
str(train_data)
## '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/ 396 levels "-0.016850","-0.021024",..: NA NA NA NA NA NA NA NA NA NA ...
## $ kurtosis_picth_belt : Factor w/ 316 levels "-0.021887","-0.060755",..: NA NA NA NA NA NA NA NA NA NA ...
## $ kurtosis_yaw_belt : Factor w/ 1 level "#DIV/0!": NA NA NA NA NA NA NA NA NA NA ...
## $ skewness_roll_belt : Factor w/ 394 levels "-0.003095","-0.010002",..: NA NA NA NA NA NA NA NA NA NA ...
## $ skewness_roll_belt.1 : Factor w/ 337 levels "-0.005928","-0.005960",..: NA NA NA NA NA NA NA NA NA NA ...
## $ skewness_yaw_belt : Factor w/ 1 level "#DIV/0!": NA NA NA NA NA NA NA NA NA NA ...
## $ 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/ 67 levels "-0.1","-0.2",..: NA NA NA NA NA NA NA NA NA NA ...
## $ 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/ 67 levels "-0.1","-0.2",..: NA NA NA NA NA NA NA NA NA NA ...
## $ 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/ 3 levels "#DIV/0!","0.00",..: NA NA NA NA NA NA NA NA NA NA ...
## $ 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/ 329 levels "-0.02438","-0.04190",..: NA NA NA NA NA NA NA NA NA NA ...
## $ kurtosis_picth_arm : Factor w/ 327 levels "-0.00484","-0.01311",..: NA NA NA NA NA NA NA NA NA NA ...
## $ kurtosis_yaw_arm : Factor w/ 394 levels "-0.01548","-0.01749",..: NA NA NA NA NA NA NA NA NA NA ...
## $ skewness_roll_arm : Factor w/ 330 levels "-0.00051","-0.00696",..: NA NA NA NA NA NA NA NA NA NA ...
## $ skewness_pitch_arm : Factor w/ 327 levels "-0.00184","-0.01185",..: NA NA NA NA NA NA NA NA NA NA ...
## $ skewness_yaw_arm : Factor w/ 394 levels "-0.00311","-0.00562",..: NA NA NA NA NA NA NA NA NA NA ...
## $ 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/ 397 levels "-0.0035","-0.0073",..: NA NA NA NA NA NA NA NA NA NA ...
## $ kurtosis_picth_dumbbell : Factor w/ 400 levels "-0.0163","-0.0233",..: NA NA NA NA NA NA NA NA NA NA ...
## $ kurtosis_yaw_dumbbell : Factor w/ 1 level "#DIV/0!": NA NA NA NA NA NA NA NA NA NA ...
## $ skewness_roll_dumbbell : Factor w/ 400 levels "-0.0082","-0.0096",..: NA NA NA NA NA NA NA NA NA NA ...
## $ skewness_pitch_dumbbell : Factor w/ 401 levels "-0.0053","-0.0084",..: NA NA NA NA NA NA NA NA NA NA ...
## $ skewness_yaw_dumbbell : Factor w/ 1 level "#DIV/0!": NA NA NA NA NA NA NA NA NA NA ...
## $ 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/ 72 levels "-0.1","-0.2",..: NA NA NA NA NA NA NA NA NA NA ...
## $ 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/ 72 levels "-0.1","-0.2",..: NA NA NA NA NA NA NA NA NA NA ...
## $ amplitude_roll_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## [list output truncated]
Remove columns with NAs
train_data <- train_data[, (colSums(is.na(train_data))==0)]
dim(train_data)
## [1] 19622 60
test_data <- test_data[, (colSums(is.na(test_data))==0)]
dim(test_data)
## [1] 20 60
str(train_data)
## 'data.frame': 19622 obs. of 60 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 ...
## $ 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 ...
## $ 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 ...
## $ 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 ...
## $ total_accel_dumbbell: int 37 37 37 37 37 37 37 37 37 37 ...
## $ gyros_dumbbell_x : num 0 0 0 0 0 0 0 0 0 0 ...
## $ gyros_dumbbell_y : num -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 ...
## $ gyros_dumbbell_z : num 0 0 0 -0.02 0 0 0 0 0 0 ...
## $ accel_dumbbell_x : int -234 -233 -232 -232 -233 -234 -232 -234 -232 -235 ...
## $ accel_dumbbell_y : int 47 47 46 48 48 48 47 46 47 48 ...
## $ accel_dumbbell_z : int -271 -269 -270 -269 -270 -269 -270 -272 -269 -270 ...
## $ magnet_dumbbell_x : int -559 -555 -561 -552 -554 -558 -551 -555 -549 -558 ...
## $ magnet_dumbbell_y : int 293 296 298 303 292 294 295 300 292 291 ...
## $ magnet_dumbbell_z : num -65 -64 -63 -60 -68 -66 -70 -74 -65 -69 ...
## $ roll_forearm : num 28.4 28.3 28.3 28.1 28 27.9 27.9 27.8 27.7 27.7 ...
## $ pitch_forearm : num -63.9 -63.9 -63.9 -63.9 -63.9 -63.9 -63.9 -63.8 -63.8 -63.8 ...
## $ yaw_forearm : num -153 -153 -152 -152 -152 -152 -152 -152 -152 -152 ...
## $ total_accel_forearm : int 36 36 36 36 36 36 36 36 36 36 ...
## $ gyros_forearm_x : num 0.03 0.02 0.03 0.02 0.02 0.02 0.02 0.02 0.03 0.02 ...
## $ gyros_forearm_y : num 0 0 -0.02 -0.02 0 -0.02 0 -0.02 0 0 ...
## $ gyros_forearm_z : num -0.02 -0.02 0 0 -0.02 -0.03 -0.02 0 -0.02 -0.02 ...
## $ accel_forearm_x : int 192 192 196 189 189 193 195 193 193 190 ...
## $ accel_forearm_y : int 203 203 204 206 206 203 205 205 204 205 ...
## $ accel_forearm_z : int -215 -216 -213 -214 -214 -215 -215 -213 -214 -215 ...
## $ magnet_forearm_x : int -17 -18 -18 -16 -17 -9 -18 -9 -16 -22 ...
## $ magnet_forearm_y : num 654 661 658 658 655 660 659 660 653 656 ...
## $ magnet_forearm_z : num 476 473 469 469 473 478 470 474 476 473 ...
## $ classe : Factor w/ 5 levels "A","B","C","D",..: 1 1 1 1 1 1 1 1 1 1 ...
Delete columns 1 to 8 because they are not relevant as predictors.
train_data <- train_data[, -c(1:8)]
dim(train_data)
## [1] 19622 52
test_data <- test_data[, -c(1:8)]
dim(test_data)
## [1] 20 52
Data processing
split the data into training and testing data
inTrain <- createDataPartition(y=train_data$classe, p=0.7, list = FALSE)
train <- train_data[inTrain,]
test <- train_data[-inTrain,]
dim(train); dim(test)
## [1] 13737 52
## [1] 5885 52
remove zero covariates
nsv <- nearZeroVar(train, saveMetrics = TRUE)
nsv
## freqRatio percentUnique zeroVar nzv
## pitch_belt 1.014085 12.38261629 FALSE FALSE
## yaw_belt 1.079772 13.03778117 FALSE FALSE
## total_accel_belt 1.047488 0.20382907 FALSE FALSE
## gyros_belt_x 1.043249 0.93906967 FALSE FALSE
## gyros_belt_y 1.135377 0.47317464 FALSE FALSE
## gyros_belt_z 1.067906 1.19385601 FALSE FALSE
## accel_belt_x 1.017921 1.14289874 FALSE FALSE
## accel_belt_y 1.144380 0.99730654 FALSE FALSE
## accel_belt_z 1.118033 2.11836646 FALSE FALSE
## magnet_belt_x 1.069388 2.17660333 FALSE FALSE
## magnet_belt_y 1.069915 2.10380724 FALSE FALSE
## magnet_belt_z 1.026549 3.16663027 FALSE FALSE
## roll_arm 53.818182 17.60209653 FALSE FALSE
## pitch_arm 87.740741 20.30283177 FALSE FALSE
## yaw_arm 31.157895 19.25456796 FALSE FALSE
## total_accel_arm 1.017081 0.48045425 FALSE FALSE
## gyros_arm_x 1.025281 4.57159496 FALSE FALSE
## gyros_arm_y 1.422043 2.64977797 FALSE FALSE
## gyros_arm_z 1.116022 1.73982675 FALSE FALSE
## accel_arm_x 1.091667 5.53978307 FALSE FALSE
## accel_arm_y 1.215278 3.79267671 FALSE FALSE
## accel_arm_z 1.056180 5.64897721 FALSE FALSE
## magnet_arm_x 1.016667 9.61636456 FALSE FALSE
## magnet_arm_y 1.129032 6.21678678 FALSE FALSE
## magnet_arm_z 1.037975 9.12135110 FALSE FALSE
## roll_dumbbell 1.120879 86.66375482 FALSE FALSE
## pitch_dumbbell 2.156863 84.35611851 FALSE FALSE
## yaw_dumbbell 1.228916 86.10322487 FALSE FALSE
## total_accel_dumbbell 1.075710 0.31302322 FALSE FALSE
## gyros_dumbbell_x 1.011601 1.67431026 FALSE FALSE
## gyros_dumbbell_y 1.320293 1.97277426 FALSE FALSE
## gyros_dumbbell_z 1.030879 1.45592196 FALSE FALSE
## accel_dumbbell_x 1.075893 2.99191963 FALSE FALSE
## accel_dumbbell_y 1.000000 3.29766325 FALSE FALSE
## accel_dumbbell_z 1.175758 2.90456432 FALSE FALSE
## magnet_dumbbell_x 1.150000 7.81830094 FALSE FALSE
## magnet_dumbbell_y 1.181818 6.01295771 FALSE FALSE
## magnet_dumbbell_z 1.007407 4.79726287 FALSE FALSE
## roll_forearm 11.502110 13.58375191 FALSE FALSE
## pitch_forearm 66.487805 19.04345927 FALSE FALSE
## yaw_forearm 16.317365 12.84851132 FALSE FALSE
## total_accel_forearm 1.134593 0.48045425 FALSE FALSE
## gyros_forearm_x 1.000000 2.02373153 FALSE FALSE
## gyros_forearm_y 1.010989 5.29955594 FALSE FALSE
## gyros_forearm_z 1.119186 2.13292568 FALSE FALSE
## accel_forearm_x 1.114754 5.65625682 FALSE FALSE
## accel_forearm_y 1.000000 7.09033996 FALSE FALSE
## accel_forearm_z 1.037736 4.01106501 FALSE FALSE
## magnet_forearm_x 1.033898 10.56999345 FALSE FALSE
## magnet_forearm_y 1.133333 13.24161025 FALSE FALSE
## magnet_forearm_z 1.024390 11.72017180 FALSE FALSE
## classe 1.469526 0.03639805 FALSE FALSE
There are no variables to drop(All FALSE)
Fitting model by Predicting outcomes using random forest method.
I used the crossvalidation method to detect relevant features for building the correct model
set.seed(123)
modfit <- train(classe~.,method="rf",trControl=trainControl(method = "cv", number = 3), data=train)
## randomForest 4.6-12
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
## The following object is masked from 'package:ggplot2':
##
## margin
modfit
## Random Forest
##
## 13737 samples
## 51 predictor
## 5 classes: 'A', 'B', 'C', 'D', 'E'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 9158, 9159, 9157
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 2 0.9870420 0.9836052
## 26 0.9887890 0.9858163
## 51 0.9842755 0.9801052
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 26.
Predict new values using the test data. This is also the expected out of sample error
pre_test <- predict(modfit, test)
confusionMatrix(test$classe, pre_test)
## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1674 0 0 0 0
## B 7 1126 5 0 1
## C 0 8 1014 4 0
## D 0 0 15 948 1
## E 0 0 2 1 1079
##
## Overall Statistics
##
## Accuracy : 0.9925
## 95% CI : (0.99, 0.9946)
## No Information Rate : 0.2856
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9905
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9958 0.9929 0.9788 0.9948 0.9981
## Specificity 1.0000 0.9973 0.9975 0.9968 0.9994
## Pos Pred Value 1.0000 0.9886 0.9883 0.9834 0.9972
## Neg Pred Value 0.9983 0.9983 0.9955 0.9990 0.9996
## Prevalence 0.2856 0.1927 0.1760 0.1619 0.1837
## Detection Rate 0.2845 0.1913 0.1723 0.1611 0.1833
## Detection Prevalence 0.2845 0.1935 0.1743 0.1638 0.1839
## Balanced Accuracy 0.9979 0.9951 0.9881 0.9958 0.9988
## the accuracy for the expected out of sample error is 99.27%
Predicting new values(20 test cases) using the prediction model developed.
pred_final <- predict(modfit, newdata=test_data)
pred_final
## [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