library(openxlsx)
library(tm)
library(car)
library(foreign)
library(readr)
library(dplyr)
library(RWeka)
library(RODBC)
library(class)
library(gmodels)
library(neuralnet)
library(kernlab)This project explores a basic application of “neural networks and optical character recognition support vector machines”. The data used are for practice and were drawn from “concrete” and “Letter” data and text: “Machine Learning with R”.
# call data
concrete<-read.xlsx("C:\\Users\\Jaire\\OneDrive\\Desktop\\Exploratory Research\\ML\\concrete.xlsx")# check data
str(concrete)## 'data.frame': 1030 obs. of 9 variables:
## $ cement : num 540 540 332 332 199 ...
## $ slag : num 0 0 142 142 132 ...
## $ ash : num 0 0 0 0 0 0 0 0 0 0 ...
## $ water : num 162 162 228 228 192 228 228 228 228 228 ...
## $ superplastic: num 2.5 2.5 0 0 0 0 0 0 0 0 ...
## $ coarseagg : num 1040 1055 932 932 978 ...
## $ fineagg : num 676 676 594 594 826 ...
## $ age : num 28 28 270 365 360 90 365 28 28 28 ...
## $ strength : num 80 61.9 40.3 41 44.3 ...# create normalize function
normalize <- function(x) {
return((x - min(x)) / (max(x) - min(x)))
}# scale data so that values are closer to zero
concrete_norm <- as.data.frame(lapply(concrete, normalize))# check normalization using target vector
summary(concrete_norm$strength)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.2664 0.4001 0.4172 0.5457 1.0000# create training (%75) and test data (%25)
concrete_train <- concrete_norm[1:773, ]
concrete_test <- concrete_norm[774:1030, ]# multilayer feedforward neural network
concrete_model <- neuralnet(strength ~ cement + slag +
ash + water + superplastic +
coarseagg + fineagg + age,
data = concrete_train)# plot newtwork typology
plot(concrete_model)The plot shows the input nodes for each feature, their weights, the hidden features, the # of steps, sum of squared errors, hidden node, and output node.
# assess model performance
model_results <- compute(concrete_model, concrete_test[1:8])
summary(model_results)## Length Class Mode
## neurons 2 -none- list
## net.result 257 -none- numericpredicted_strength <- model_results$net.resultThe model has two layers and 257 predicted values.
# check correlation between predicted and actual concrete strength.
cor(predicted_strength, concrete_test$strength)## [,1]
## [1,] 0.7124602The correlation between the predicted and actual concrete strength is strong.
# increade number of hidden nodes to 10
concrete_model2 <- neuralnet(strength ~ cement + slag +
ash + water + superplastic +
coarseagg + fineagg + age,
data = concrete_train, hidden = 10)# plot newtwork 2 typology
plot(concrete_model2)Whoa! reduced sum of squared errors and more steps.
# check feedback
model_results2 <- compute(concrete_model2, concrete_test[1:8])
predicted_strength2 <- model_results2$net.result
cor(predicted_strength2, concrete_test$strength)## [,1]
## [1,] 0.7095803The correlation between the predicted and actual concrete strength is stronger.
# call data
letters<-read.xlsx("C:\\Users\\Jaire\\OneDrive\\Desktop\\Exploratory Research\\ML\\letterdata.xlsx")# check data
str(letters)## 'data.frame': 20000 obs. of 17 variables:
## $ letter: chr "T" "I" "D" "N" ...
## $ xbox : num 2 5 4 7 2 4 4 1 2 11 ...
## $ ybox : num 8 12 11 11 1 11 2 1 2 15 ...
## $ width : num 3 3 6 6 3 5 5 3 4 13 ...
## $ height: num 5 7 8 6 1 8 4 2 4 9 ...
## $ onpix : num 1 2 6 3 1 3 4 1 2 7 ...
## $ xbar : num 8 10 10 5 8 8 8 8 10 13 ...
## $ ybar : num 13 5 6 9 6 8 7 2 6 2 ...
## $ x2bar : num 0 5 2 4 6 6 6 2 2 6 ...
## $ y2bar : num 6 4 6 6 6 9 6 2 6 2 ...
## $ xybar : num 6 13 10 4 6 5 7 8 12 12 ...
## $ x2ybar: num 10 3 3 4 5 6 6 2 4 1 ...
## $ xy2bar: num 8 9 7 10 9 6 6 8 8 9 ...
## $ xedge : num 0 2 3 6 1 0 2 1 1 8 ...
## $ xedgey: num 8 8 7 10 7 8 8 6 6 1 ...
## $ yedge : num 0 4 3 2 5 9 7 2 1 1 ...
## $ yedgex: num 8 10 9 8 10 7 10 7 7 8 ...*Normalization occurs by default.
# convert target response to factor
letters$letter<-as.factor(letters$letter)
is.factor(letters$letter)## [1] TRUE# create training (%80) and test data (%20)
letters_train <- letters[1:16000, ]
letters_test <- letters[16001:20000, ]# train data
letter_classifier <- ksvm(letter ~ ., data = letters_train,
kernel = "vanilladot")## Setting default kernel parametersletter_classifier## Support Vector Machine object of class "ksvm"
##
## SV type: C-svc (classification)
## parameter : cost C = 1
##
## Linear (vanilla) kernel function.
##
## Number of Support Vectors : 7037
##
## Objective Function Value : -14.1746 -20.0072 -23.5628 -6.2009 -7.5524 -32.7694 -49.9786 -18.1824 -62.1111 -32.7284 -16.2209 -32.2837 -28.9777 -51.2195 -13.276 -35.6217 -30.8612 -16.5256 -14.6811 -32.7475 -30.3219 -7.7956 -11.8138 -32.3463 -13.1262 -9.2692 -153.1654 -52.9678 -76.7744 -119.2067 -165.4437 -54.6237 -41.9809 -67.2688 -25.1959 -27.6371 -26.4102 -35.5583 -41.2597 -122.164 -187.9178 -222.0856 -21.4765 -10.3752 -56.3684 -12.2277 -49.4899 -9.3372 -19.2092 -11.1776 -100.2186 -29.1397 -238.0516 -77.1985 -8.3339 -4.5308 -139.8534 -80.8854 -20.3642 -13.0245 -82.5151 -14.5032 -26.7509 -18.5713 -23.9511 -27.3034 -53.2731 -11.4773 -5.12 -13.9504 -4.4982 -3.5755 -8.4914 -40.9716 -49.8182 -190.0269 -43.8594 -44.8667 -45.2596 -13.5561 -17.7664 -87.4105 -107.1056 -37.0245 -30.7133 -112.3218 -32.9619 -27.2971 -35.5836 -17.8586 -5.1391 -43.4094 -7.7843 -16.6785 -58.5103 -159.9936 -49.0782 -37.8426 -32.8002 -74.5249 -133.3423 -11.1638 -5.3575 -12.438 -30.9907 -141.6924 -54.2953 -179.0114 -99.8896 -10.288 -15.1553 -3.7815 -67.6123 -7.696 -88.9304 -47.6448 -94.3718 -70.2733 -71.5057 -21.7854 -12.7657 -7.4383 -23.502 -13.1055 -239.9708 -30.4193 -25.2113 -136.2795 -140.9565 -9.8122 -34.4584 -6.3039 -60.8421 -66.5793 -27.2816 -214.3225 -34.7796 -16.7631 -135.7821 -160.6279 -45.2949 -25.1023 -144.9059 -82.2352 -327.7154 -142.0613 -158.8821 -32.2181 -32.8887 -52.9641 -25.4937 -47.9936 -6.8991 -9.7293 -36.436 -70.3907 -187.7611 -46.9371 -89.8103 -143.4213 -624.3645 -119.2204 -145.4435 -327.7748 -33.3255 -64.0607 -145.4831 -116.5903 -36.2977 -66.3762 -44.8248 -7.5088 -217.9246 -12.9699 -30.504 -2.0369 -6.126 -14.4448 -21.6337 -57.3084 -20.6915 -184.3625 -20.1052 -4.1484 -4.5344 -0.828 -121.4411 -7.9486 -58.5604 -21.4878 -13.5476 -5.646 -15.629 -28.9576 -20.5959 -76.7111 -27.0119 -94.7101 -15.1713 -10.0222 -7.6394 -1.5784 -87.6952 -6.2239 -99.3711 -101.0906 -45.6639 -24.0725 -61.7702 -24.1583 -52.2368 -234.3264 -39.9749 -48.8556 -34.1464 -20.9664 -11.4525 -123.0277 -6.4903 -5.1865 -8.8016 -9.4618 -21.7742 -24.2361 -123.3984 -31.4404 -88.3901 -30.0924 -13.8198 -9.2701 -3.0823 -87.9624 -6.3845 -13.968 -65.0702 -105.523 -13.7403 -13.7625 -50.4223 -2.933 -8.4289 -80.3381 -36.4147 -112.7485 -4.1711 -7.8989 -1.2676 -90.8037 -21.4919 -7.2235 -47.9557 -3.383 -20.433 -64.6138 -45.5781 -56.1309 -6.1345 -18.6307 -2.374 -72.2553 -111.1885 -106.7664 -23.1323 -19.3765 -54.9819 -34.2953 -64.4756 -20.4115 -6.689 -4.378 -59.141 -34.2468 -58.1509 -33.8665 -10.6902 -53.1387 -13.7478 -20.1987 -55.0923 -3.8058 -60.0382 -235.4841 -12.6837 -11.7407 -17.3058 -9.7167 -65.8498 -17.1051 -42.8131 -53.1054 -25.0437 -15.302 -44.0749 -16.9582 -62.9773 -5.204 -5.2963 -86.1704 -3.7209 -6.3445 -1.1264 -122.5771 -23.9041 -355.0145 -31.1013 -32.619 -4.9664 -84.1048 -134.5957 -72.8371 -23.9002 -35.3077 -11.7119 -22.2889 -1.8598 -59.2174 -8.8994 -150.742 -1.8533 -1.9711 -9.9676 -0.5207 -26.9229 -30.429 -5.6289
## Training error : 0.130062# check model performance
letter_predictions <- predict(letter_classifier, letters_test)
# check predictions
head(letter_predictions)## [1] U N V X N H
## Levels: A B C D E F G H I J K L M N O P Q R S T U V W X Y Z# compared predicted response to actual responses
table(letter_predictions, letters_test$letter)##
## letter_predictions A B C D E F G H I J K L M N O
## A 144 0 0 0 0 0 0 0 0 1 0 0 1 2 2
## B 0 121 0 5 2 0 1 2 0 0 1 0 1 0 0
## C 0 0 120 0 4 0 10 2 2 0 1 3 0 0 2
## D 2 2 0 156 0 1 3 10 4 3 4 3 0 5 5
## E 0 0 5 0 127 3 1 1 0 0 3 4 0 0 0
## F 0 0 0 0 0 138 2 2 6 0 0 0 0 0 0
## G 1 1 2 1 9 2 123 2 0 0 1 2 1 0 1
## H 0 0 0 1 0 1 0 102 0 2 3 2 3 4 20
## I 0 1 0 0 0 1 0 0 141 8 0 0 0 0 0
## J 0 1 0 0 0 1 0 2 5 128 0 0 0 0 1
## K 1 1 9 0 0 0 2 5 0 0 118 0 0 2 0
## L 0 0 0 0 2 0 1 1 0 0 0 133 0 0 0
## M 0 0 1 1 0 0 1 1 0 0 0 0 135 4 0
## N 0 0 0 0 0 1 0 1 0 0 0 0 0 145 0
## O 1 0 2 1 0 0 1 2 0 1 0 0 0 1 99
## P 0 0 0 1 0 2 1 0 0 0 0 0 0 0 2
## Q 0 0 0 0 0 0 8 2 0 0 0 3 0 0 3
## R 0 7 0 0 1 0 3 8 0 0 13 0 0 1 1
## S 1 1 0 0 1 0 3 0 1 1 0 1 0 0 0
## T 0 0 0 0 3 2 0 0 0 0 1 0 0 0 0
## U 1 0 3 1 0 0 0 2 0 0 0 0 0 0 1
## V 0 0 0 0 0 1 3 4 0 0 0 0 1 2 1
## W 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0
## X 0 1 0 0 2 0 0 1 3 0 1 6 0 0 1
## Y 3 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## Z 2 0 0 0 1 0 0 0 3 4 0 0 0 0 0
##
## letter_predictions P Q R S T U V W X Y Z
## A 0 5 0 1 1 1 0 1 0 0 1
## B 2 2 3 5 0 0 2 0 1 0 0
## C 0 0 0 0 0 0 0 0 0 0 0
## D 3 1 4 0 0 0 0 0 3 3 1
## E 0 2 0 10 0 0 0 0 2 0 3
## F 16 0 0 3 0 0 1 0 1 2 0
## G 2 8 2 4 3 0 0 0 1 0 0
## H 0 2 3 0 3 0 2 0 0 1 0
## I 1 0 0 3 0 0 0 0 5 1 1
## J 1 3 0 2 0 0 0 0 1 0 6
## K 1 0 7 0 1 3 0 0 5 0 0
## L 0 1 0 5 0 0 0 0 0 0 1
## M 0 0 0 0 0 3 0 8 0 0 0
## N 0 0 3 0 0 1 0 2 0 0 0
## O 3 3 0 0 0 3 0 0 0 0 0
## P 130 0 0 0 0 0 0 0 0 1 0
## Q 1 124 0 5 0 0 0 0 0 2 0
## R 1 0 138 0 1 0 1 0 0 0 0
## S 0 14 0 101 3 0 0 0 2 0 10
## T 0 0 0 3 133 1 0 0 0 2 2
## U 0 0 0 0 0 152 0 0 1 1 0
## V 0 3 1 0 0 0 126 1 0 4 0
## W 0 0 0 0 0 4 4 127 0 0 0
## X 0 0 0 1 0 0 0 0 137 1 1
## Y 7 0 0 0 3 0 0 0 0 127 0
## Z 0 0 0 18 3 0 0 0 0 0 132The diagonal values show the number of records where the predicted response matches the true response.The values within columns show incorrect response predictions.
# assess overall accuracy
agreement <- letter_predictions == letters_test$letter
table(agreement)## agreement
## FALSE TRUE
## 643 3357prop.table(table(agreement))## agreement
## FALSE TRUE
## 0.16075 0.83925The model has an accuracy rate of nearly 84%.
# improvements to model performance using different kernel mapping
letter_classifier_rbf <- ksvm(letter ~ ., data = letters_train,
kernel = "rbfdot")# make new predictions
letter_predictions_rbf <- predict(letter_classifier_rbf,
letters_test)# assess new model's accuracy
agreement_rbf <- letter_predictions_rbf == letters_test$letter
table(agreement_rbf)## agreement_rbf
## FALSE TRUE
## 277 3723prop.table(table(agreement_rbf))## agreement_rbf
## FALSE TRUE
## 0.06925 0.93075The new model has an accuracy rate of 93%; almost a 10% increase in accuracy.