Decision Trees and Naïve Bayes Quiz

What is the main goal of a decision tree in classification?

• To cluster data points

• To find hidden patterns in data

• To split data into subsets based on attribute values

• To optimize database queries

Which criterion is commonly used for splitting nodes in a decision tree?

• Mean Squared Error

• Entropy and Information Gain

• Support Vectors

• Gradient Descent

What happens when a decision tree model is too deep?

• The model underfits the data

• The model generalizes better

• The model overfits the data

• The model reduces bias completely

What is pruning in a decision tree?

• Adding more branches to a tree

• Removing nodes to reduce overfitting

• Increasing tree depth for better accuracy

• A method to grow a tree dynamically

Which measure is used in the Gini Index?

• Probability of misclassification

• Entropy calculation

• Mean of attribute values

• Distance between data points

When does a decision tree stop growing?

• When all attributes are used

• When entropy is maximum

• When no further splits improve classification

• When there are too many leaf nodes

Which of the following is NOT an advantage of decision trees?

• Easy to interpret

• Handles non-linear relationships well

• Works well with large datasets

• Requires no feature selection

How does overfitting occur in a decision tree?

• By making the tree too simple

• By pruning the tree too much

• By making the tree too complex

• By using fewer training samples

What is the purpose of the entropy function in decision trees?

• To measure the randomness in data

• To optimize computational efficiency

• To define the maximum depth of the tree

• To reduce bias in predictions

Which technique can be used to handle continuous-valued attributes in decision trees?

• Binning

• Feature Scaling

• Naïve Bayes Classification

• Data Augmentation