IML-CT6

What is the primary purpose of decision trees in machine learning?

• Data preprocessing

• Feature extraction

• Classification and regression tasks

• Data visualization

What does the root node of a decision tree represent?

• A terminal decision

• A feature test

• The entire dataset

• A split condition

What is a disadvantage of decision trees?

• They can overfit the training data

• They are difficult to interpret

• They are non-parametric

• They handle categorical data poorly

Which of the following is NOT a measure used in computing information gain?

• Entropy

• Gini Index

• Classification Error

• Standard Deviation

How do decision trees handle numerical features?

• By using one-vs-all splits

• By evaluating threshold values

• By ignoring them

• By converting them to categorical

What is the main goal of maximizing information gain in decision trees?

• To reduce uncertainty or impurity

• To increase the number of features

• To enhance data visualization

• To minimize the tree depth

What is a leaf node in a decision tree?

• A node that splits the dataset

• A node that connects two branches

• A node that represents a feature test

• A terminal node providing the output

What happens when a decision tree is overfitted?

• It has fewer nodes

• It performs poorly on new data

• It becomes too simple

• It performs well on new data

Which of the following is a characteristic of decision trees?

• They are always linear

• They cannot handle categorical data

• They are sensitive to small changes in data

• They require extensive preprocessing

What is the effect of pruning in decision trees?

• It increases the tree depth

• It reduces overfitting

• It enhances data complexity

• It improves training speed