decision trees

What is the primary purpose of a decision tree in machine learning?

• To perform linear regression

• To predict the output based on input features

• To organize data in a table format

• To reduce the complexity of the dataset

In a decision tree, what does each internal node represent?

• The final output of the model

• A feature used to split the data

• A leaf node with a classification label

• A random decision made by the tree

What does a leaf node in a decision tree represent?

• A decision point where the dataset is split

• A final classification or output value

• A threshold value used for splitting

• A weight used to adjust the tree’s behavior

Which of the following is a common problem that decision trees face?

• Overfitting

• Underfitting

• Lack of interpretability

• Lack of data points

In decision trees, what does the term “pruning” refer to?

• Adding more branches to improve accuracy

• Removing unnecessary branches to prevent overfitting

• Recalculating the root node

• Randomly selecting features for splitting

Which algorithm is typically used to create decision trees?

• k-means clustering

• Naive Bayes

• ID3

• K-nearest neighbors

Which metric is commonly used to evaluate the performance of a decision tree model?

• Mean Squared Error

• Accuracy

• Cross-entropy loss

• Area under the curve (AUC)

How does a decision tree handle categorical features?

• It converts categorical features into continuous values

• It splits categorical data based on unique values

• It ignores categorical features entirely

• It uses one-hot encoding for categorical features

What is the effect of increasing the maximum depth of a decision tree?

• The tree becomes more generalized

• The tree is less likely to overfit

• The tree becomes more complex and may overfit

• • The tree's training time decreases