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Decision Trees in R, Decision trees are mainly classification and regression types.

Classification means Y variable is factor and regression type means Y variable is numeric.

Just look at one of the examples from each type,

Classification example is detecting email spam data and regression tree example is from Boston housing data.

Decision trees are also called Trees and CART.

CART indicates classification and regression trees.

The main goal behind classification tree is to classify or predict an outcome based on a set of predictors.

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Easy Interpretation

Making prediction is fast

Easy to identify important variables

Handless missing data

One of the drawbacks is to can have high variability in performance.

Recursive portioning- basis can achieve maximum homogeneity within the new partition.

Discriminant Analysis in R

## Decision Trees in R

### Method 1:- Classification Tree

library(DAAG)
library(party)
library(rpart)
library(rpart.plot)
library(mlbench)
library(caret)
library(pROC)
library(tree)

#### Getting Data -Email Spam Detection

str(spam7)
data.frame':  4601 obs. of  7 variables:
$crl.tot: num 278 1028 2259 191 191 ...$ dollar : num  0 0.18 0.184 0 0 0 0.054 0 0.203 0.081 ...
$bang : num 0.778 0.372 0.276 0.137 0.135 0 0.164 0 0.181 0.244 ...$ money  : num  0 0.43 0.06 0 0 0 0 0 0.15 0 ...
$n000 : num 0 0.43 1.16 0 0 0 0 0 0 0.19 ...$ make   : num  0 0.21 0.06 0 0 0 0 0 0.15 0.06 ...
$yesno : Factor w/ 2 levels "n","y": 2 2 2 2 2 2 2 2 2 2 ... Total 4601 observations and 7 variables. Chi Square Distribution Examples mydata <- spam7 #### Data Partition set.seed(1234) ind <- sample(2, nrow(mydata), replace = T, prob = c(0.5, 0.5)) train <- mydata[ind == 1,] test <- mydata[ind == 2,] Tree Classification tree <- rpart(yesno ~., data = train) rpart.plot(tree) printcp(tree) Classification tree: rpart(formula = yesno ~ ., data = train) Variables actually used in tree construction: [1] bang crl.tot dollar Root node error: 900/2305 = 0.39046 n= 2305 CP nsplit rel error xerror xstd 1 0.474444 0 1.00000 1.00000 0.026024 2 0.074444 1 0.52556 0.56556 0.022128 3 0.010000 3 0.37667 0.42111 0.019773 plotcp(tree)  You can change the cp value according to your data set. Please note lower cp value means bigger the tree. If you are using too lower cp that leads to overfitting also. tree <- rpart(yesno ~., data = train,cp=0.07444) #### Confusion matrix -train p <- predict(tree, train, type = 'class') confusionMatrix(p, train$yesno, positive=’y’)

Please make sure you mention positive classes in the confusion matrix.

Random Forest Model in R

Confusion Matrix and Statistics
Reference
Prediction    n    y
n 1278  212
y  127  688
Accuracy : 0.8529
95% CI : (0.8378, 0.8671)
No Information Rate : 0.6095
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.6857
Mcnemar's Test P-Value : 5.061e-06
Sensitivity : 0.7644
Specificity : 0.9096
Pos Pred Value : 0.8442
Neg Pred Value : 0.8577
Prevalence : 0.3905
Detection Rate : 0.2985
Detection Prevalence : 0.3536
Balanced Accuracy : 0.8370
'Positive' Class : y
Model has 85% accuracy

#### ROC

p1 <- predict(tree, test, type = 'prob')
p1 <- p1[,2]
r <- multiclass.roc(test$yesno, p1, percent = TRUE) roc <- r[['rocs']] r1 <- roc[[1]] plot.roc(r1, print.auc=TRUE, auc.polygon=TRUE, grid=c(0.1, 0.2), grid.col=c("green", "red"), max.auc.polygon=TRUE, auc.polygon.col="lightblue", print.thres=TRUE, main= 'ROC Curve')  ### Method 2- Regression Tree data('BostonHousing') mydata <- BostonHousing Market Basket Analysis in R #### Data Partition set.seed(1234) ind <- sample(2, nrow(mydata), replace = T, prob = c(0.5, 0.5)) train <- mydata[ind == 1,] test <- mydata[ind == 2,] Regression tree tree <- rpart(medv ~., data = train) rpart.plot(tree)  printcp(tree) Regression tree: rpart(formula = medv ~ ., data = train) Variables actually used in tree construction: [1] age crim lstat rm Root node error: 22620/262 = 86.334 n= 262 CP nsplit rel error xerror xstd 0.469231 0 1.00000 1.01139 0.115186 2 0.128700 1 0.53077 0.62346 0.080154 3 0.098630 2 0.40207 0.51042 0.076055 4 0.033799 3 0.30344 0.42674 0.069827 5 0.028885 4 0.26964 0.39232 0.066342 6 0.028018 5 0.24075 0.37848 0.066389 7 0.015141 6 0.21274 0.34877 0.065824 8 0.010000 7 0.19760 0.33707 0.065641 rpart.rules(tree) medv 13 when lstat >= 14.8 & crim >= 5.8 17 when lstat >= 14.8 & crim < 5.8 22 when lstat is 7.2 to 14.8 & rm < 6.6 26 when lstat < 7.2 & rm < 6.8 & age < 89 29 when lstat is 7.2 to 14.8 & rm >= 6.6 33 when lstat < 7.2 & rm is 6.8 to 7.5 & age < 89 40 when lstat < 7.2 & rm < 7.5 & age >= 89 45 when lstat < 7.2 & rm >= 7.5  plotcp(tree)  #### Predict p <- predict(tree, train) Root Mean Square Error sqrt(mean((train$medv-p)^2))

4.130294

R Square

(cor(train\$medv,p))^2

0.8024039

## Conclusion

In the regression model, the r square value is 80% and RMSE is 4.13, not bad at all..In this way, you can make use of Decision classification regression tree models.