Working principle of Decision Tree

What is the primary purpose of a decision tree classifier?

• To perform clustering

• To classify data into categories

• To predict continuous values

• To optimize algorithms

How does a decision tree determine the best feature to split the data?

• By user input

• By random selection

• By evaluating the purity of subsets

• By using a fixed sequence

What is a 'pure subset' in the context of decision trees?

• A subset with only one outcome

• A subset with equal outcomes

• A subset with mixed outcomes

• A subset with no data

Which node in a decision tree is responsible for starting the decision-making process?

• Leaf node

• Root node

• Internal node

• Branch node

What is the main advantage of using a random forest over a single decision tree?

• It provides more accurate predictions

• It uses fewer features

• It is faster to train

• It requires less data

How does a random forest make a prediction?

• By averaging the predictions of all trees

• By using the prediction with the least votes

• By selecting the prediction of the first tree

• By choosing a random prediction

What is a potential reason for a decision tree to make incorrect predictions?

• The data is too simple

• The training data is insufficient

• The algorithm is too complex

• The features are too few