KDD MCQ-2

What is the primary purpose of a decision tree?

• To visualize data

• To classify or predict outcomes based on input features

• To perform regression analysis

• To cluster data points

Which of the following best describes a leaf node in a decision tree?

• A node that represents a decision point

• A node that contains the final outcome or class label

• A node that connects two branches

• A node that splits the dataset

What algorithm is commonly used to construct decision trees?

• K-Means

• ID3

• Naive Bayes

• Linear Regression

In decision trees, what does "information gain" measure?

• The amount of data processed

• The reduction in uncertainty after a dataset is split on an attribute

• The total number of nodes in the tree

• The total number of nodes in the tree

Which criterion is NOT typically used for splitting nodes in decision trees?

• Gini impurity

• Entropy

• Mean Squared Error

• Information Gain

What is a common disadvantage of decision trees?

• They are difficult to interpret.

• They can easily overfit the training data.

• They require extensive data preprocessing.

• They are only suitable for numerical data.

Which of the following techniques can help reduce overfitting in decision trees?

• Increasing tree depth

• Pruning branches

• Adding more features

• Decreasing sample size

What type of decision tree predicts continuous values?

• Classification tree

• Regression tree

• Clustering tree

• Ensemble tree

In the context of decision trees, what does "pruning" refer to?

• Adding more nodes to increase complexity

• Removing branches that do not provide significant predictive power

• Splitting nodes further for better accuracy

• Combining multiple trees into one model

Which method can be used to evaluate the performance of a decision tree model?

• Cross-validation

• Linear regression analysis

• K-nearest neighbors algorithm

• Principal component analysis