Final answer:
To construct a Decision Tree for a binary target in R, one must prepare the dataset, use an appropriate package like rpart, fit the model, summarize and visualize the results, and perform model tuning to prevent overfitting.
Step-by-step explanation:
The question asks how to construct a Decision Tree for a binary target using R-Code, which is a common task in machine learning within the field of Computers and Technology. The process involves several steps, which include preparing the dataset, splitting it into training and test datasets, using an algorithm like C4.5, CART, or random forest, training the model on the training dataset, and validating the model using the test dataset.
To construct the Decision Tree in R, you would typically use packages like rpart, party, or randomForest, depending on the algorithm you choose. The code would look something like this:
library(rpart)
data <- your_data_frame # your data
fit <- rpart(Target~., data=data_train, method="class")
summary(fit) # displays the results
Replace your_data_frame with your actual dataset, and Target with your binary target variable. After fitting the model, you can visualize the tree using various plotting functions, and make predictions on new data.
It's important to also perform model tuning to adjust the complexity of the tree and avoid overfitting, ensuring that the model generalizes well to new data.