Decision tree adalah salah satu algoritma machine learning yang digunakan untuk klasifikasi dan regresi. Algoritma ini membangun sebuah struktur pohon keputusan berdasarkan fitur-fitur dari data input dan labelnya (atau nilai target). Pohon keputusan ini dapat digunakan untuk membuat prediksi terhadap data yang baru.
Library
Data
Deskripsi Data
Data yang digunakan adalah data OJ dari package ISLR ,
Data tersebut berisi 1070 pembelian dimana pelanggan membeli
Citrus Hill atau Minute Maid Orange Juice. Sejumlah
karakteristik pelanggan dan produk dicatat. Data OJ memiliki 18 variabel
sebagai berikut:
Purchase A factor with levels CH and MM indicating
whether the customer purchased Citrus Hill or Minute Maid Orange
Juice
WeekofPurchase Week of purchase
StoreID Store ID
PriceCH Price charged for CH
PriceMM Price charged for MM
DiscCH Discount offered for CH
DiscMM Discount offered for MM
SpecialCH Indicator of special on CH
SpecialMM Indicator of special on MM
LoyalCH Customer brand loyalty for CH
SalePriceMM Sale price for MM
SalePriceCH Sale price for CH
PriceDiff Sale price of MM less sale price of CH
Store7 A factor with levels No and Yes indicating
whether the sale is at Store 7
PctDiscMM Percentage discount for MM
PctDiscCH Percentage discount for CH
ListPriceDiff List price of MM less list price of CH
STORE Which of 5 possible stores the sale occured at
## Purchase WeekofPurchase StoreID PriceCH PriceMM DiscCH DiscMM SpecialCH
## 1 CH 237 1 1.75 1.99 0.00 0.0 0
## 2 CH 239 1 1.75 1.99 0.00 0.3 0
## 3 CH 245 1 1.86 2.09 0.17 0.0 0
## 4 MM 227 1 1.69 1.69 0.00 0.0 0
## 5 CH 228 7 1.69 1.69 0.00 0.0 0
## 6 CH 230 7 1.69 1.99 0.00 0.0 0
## SpecialMM LoyalCH SalePriceMM SalePriceCH PriceDiff Store7 PctDiscMM
## 1 0 0.500000 1.99 1.75 0.24 No 0.000000
## 2 1 0.600000 1.69 1.75 -0.06 No 0.150754
## 3 0 0.680000 2.09 1.69 0.40 No 0.000000
## 4 0 0.400000 1.69 1.69 0.00 No 0.000000
## 5 0 0.956535 1.69 1.69 0.00 Yes 0.000000
## 6 1 0.965228 1.99 1.69 0.30 Yes 0.000000
## PctDiscCH ListPriceDiff STORE
## 1 0.000000 0.24 1
## 2 0.000000 0.24 1
## 3 0.091398 0.23 1
## 4 0.000000 0.00 1
## 5 0.000000 0.00 0
## 6 0.000000 0.30 0## Purchase WeekofPurchase StoreID PriceCH PriceMM
## CH:653 Min. :227.0 Min. :1.00 Min. :1.690 Min. :1.690
## MM:417 1st Qu.:240.0 1st Qu.:2.00 1st Qu.:1.790 1st Qu.:1.990
## Median :257.0 Median :3.00 Median :1.860 Median :2.090
## Mean :254.4 Mean :3.96 Mean :1.867 Mean :2.085
## 3rd Qu.:268.0 3rd Qu.:7.00 3rd Qu.:1.990 3rd Qu.:2.180
## Max. :278.0 Max. :7.00 Max. :2.090 Max. :2.290
## DiscCH DiscMM SpecialCH SpecialMM
## Min. :0.00000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.00000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.05186 Mean :0.1234 Mean :0.1477 Mean :0.1617
## 3rd Qu.:0.00000 3rd Qu.:0.2300 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :0.50000 Max. :0.8000 Max. :1.0000 Max. :1.0000
## LoyalCH SalePriceMM SalePriceCH PriceDiff Store7
## Min. :0.000011 Min. :1.190 Min. :1.390 Min. :-0.6700 No :714
## 1st Qu.:0.325257 1st Qu.:1.690 1st Qu.:1.750 1st Qu.: 0.0000 Yes:356
## Median :0.600000 Median :2.090 Median :1.860 Median : 0.2300
## Mean :0.565782 Mean :1.962 Mean :1.816 Mean : 0.1465
## 3rd Qu.:0.850873 3rd Qu.:2.130 3rd Qu.:1.890 3rd Qu.: 0.3200
## Max. :0.999947 Max. :2.290 Max. :2.090 Max. : 0.6400
## PctDiscMM PctDiscCH ListPriceDiff STORE
## Min. :0.0000 Min. :0.00000 Min. :0.000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.140 1st Qu.:0.000
## Median :0.0000 Median :0.00000 Median :0.240 Median :2.000
## Mean :0.0593 Mean :0.02731 Mean :0.218 Mean :1.631
## 3rd Qu.:0.1127 3rd Qu.:0.00000 3rd Qu.:0.300 3rd Qu.:3.000
## Max. :0.4020 Max. :0.25269 Max. :0.440 Max. :4.000Eksplorasi Data
library(dplyr)
library(ggplot2)
plotdata <- data_OJ %>%
count(Purchase ) %>%
arrange(desc(Purchase )) %>%
mutate(prop = round(n*100/sum(n), 1),
lab.ypos = cumsum(prop) - 0.5*prop)
# Pie Chart
ggplot(plotdata, aes(x = "", y = prop, fill = Purchase )) +
geom_bar(width = 1, stat = "identity", color = "white") +
coord_polar("y", start = 0)+
geom_text(aes(y = lab.ypos, label = prop), color = "black")+
theme_void()+
labs(title = "Persentase Purchase")
Partisi Data
set.seed(123)
# Set the proportion of data to be used for training
train_proportion <- 0.8
# Create the train/test indices with stratified sampling
train_index <- createDataPartition(data_OJ$Purchase, p = train_proportion, list = FALSE, times = 1)
# Split the data into training and testing sets
train_set1 <- data_OJ[train_index, ]
test_set1 <- data_OJ[-train_index, ]Decision Tree
set.seed(123)
dataoj_class <- rpart(formula = Purchase ~ .,
data = train_set1,
method = "class") # classification (not regression))
rpart.plot(dataoj_class, yesno = TRUE)
prediksi_test <- predict(dataoj_class, test_set1, type = "class")
plot(test_set1$Purchase, prediksi_test,
main = "Simple Classification: Predicted vs. Actual",
xlab = "Actual",
ylab = "Predicted")
## Confusion Matrix and Statistics
##
## Reference
## Prediction CH MM
## CH 109 23
## MM 21 60
##
## Accuracy : 0.7934
## 95% CI : (0.7328, 0.8457)
## No Information Rate : 0.6103
## P-Value [Acc > NIR] : 8.319e-09
##
## Kappa : 0.5638
##
## Mcnemar's Test P-Value : 0.8802
##
## Sensitivity : 0.8385
## Specificity : 0.7229
## Pos Pred Value : 0.8258
## Neg Pred Value : 0.7407
## Prevalence : 0.6103
## Detection Rate : 0.5117
## Detection Prevalence : 0.6197
## Balanced Accuracy : 0.7807
##
## 'Positive' Class : CH
##