Which of the following is a disadvantage of using decision trees for model selec
Practice Questions
Q1
Which of the following is a disadvantage of using decision trees for model selection?
They are easy to interpret
They can easily overfit the training data
They handle both numerical and categorical data
They require less data preprocessing
Questions & Step-by-Step Solutions
Which of the following is a disadvantage of using decision trees for model selection?
Step 1: Understand what a decision tree is. A decision tree is a model that makes decisions based on asking a series of questions about the data.
Step 2: Learn about overfitting. Overfitting happens when a model learns the training data too well, including its noise and outliers, making it perform poorly on new data.
Step 3: Recognize that decision trees can become very complex. If a decision tree has too many branches (is too deep), it can start to memorize the training data instead of learning general patterns.
Step 4: Know that pruning is a technique used to simplify a decision tree. If a tree is not pruned, it may keep unnecessary branches that lead to overfitting.
Step 5: Conclude that a disadvantage of decision trees is their tendency to overfit the training data, especially if they are not pruned or are too deep.
