7.Performance comparison of Regression Model, Regression Tree, Bagging Tree and Random Forest.Rmd

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DATA STRUCTURE
```{r}
data <- DALEX::dragons
head(data)
str(data)
summary(data)
```


SPLITTING DATA
```{r}
library(caret)
set.seed(2380)
index <- createDataPartition(data$year_of_birth, p = 0.75,
list = FALSE, times = 1)
train <- data[index,]
test <- data[-index,]
nrow(train)
nrow(test)
```

LINEAR REGRESSİON MODEL

```{r}
#Traing Model
model_LM <- lm(life_length~. ,data=train)
#Performance for Train
predicted_train_LM <- predict(model_LM, train)
rmse_train_LM <-sqrt(mean((predicted_train_LM - train$life_length) ^ 2))

#Performance for Test
predicted_test_LM <- predict(model_LM, test)
rmse_test_LM <- sqrt(mean((predicted_test_LM - test$life_length) ^ 2))

cat("test_rmse:", rmse_test_LM,"\n")
cat("train_rmse:", rmse_train_LM)
```

REGRESSION TREE
```{r}
# Training a regression tree on the dragons data
library(rpart)
library(rpart.plot)
model_DT <- rpart(life_length ~. , method = "anova", data = train)

#Performance for test
predicted_test_DT <- predict(model_DT, test)
rmse_test_DT <- sqrt(mean((predicted_test_DT - test$life_length) ^ 2))

#Performance for train
predicted_train_DT <- predict(model_DT, train)
rmse_train_DT <- sqrt(mean((predicted_train_DT - train$life_length) ^ 2))

cat("test_rmse_dt:", rmse_test_DT,"\n")
cat("train_rmse_dt:", rmse_train_DT)
```


TRAINING BEGGING TREE
```{r}
library(randomForest)
model_BT <- randomForest(life_length~. , data=train, mtry= 7)

#Performance for test
predicted_test_BT <- predict(model_BT, test)
rmse_test_BT <- sqrt(mean((predicted_test_BT - test$life_length) ^ 2))

#Performance for train
predicted_train_BT <- predict(model_BT, train)
rmse_train_BT <- sqrt(mean((predicted_train_BT - train$life_length) ^ 2))

cat("test_rmse_dt:", rmse_test_BT,"\n")
cat("train_rmse_dt:", rmse_train_BT)

```

RANDOM FOREST

```{r}
model_RF <- randomForest(life_length ~ ., data = train)
#Performance for test
predicted_test_RF <- predict(model_RF, test)
rmse_test_RF <- sqrt(mean((predicted_test_RF - test$life_length) ^ 2))

#Performance for train
predicted_train_RF <- predict(model_RF, train)
rmse_train_RF <- sqrt(mean((predicted_train_RF - train$life_length) ^ 2))

cat("test_rmse_dt:", rmse_test_RF,"\n")
cat("train_rmse_dt:", rmse_train_RF)
```



```{r}
#Comparing models performances
data.frame(
  "TEST" = c(rmse_test_BT,rmse_test_DT,rmse_test_LM,rmse_test_RF),
  "TRAIN" = c(rmse_train_BT,rmse_train_DT,rmse_train_LM,rmse_train_RF),
  row.names = c("BEGGING TREE", "DECISION TREE","LINEAR MODEL","RANDOM FOREST")
)
```