Which of the following is a disadvantage of using decision trees for model selec

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Question: Which of the following is a disadvantage of using decision trees for model selection?

Options:

Correct Answer: They can easily overfit the training data

Solution:

Decision trees can easily overfit the training data, especially if they are not pruned or if the tree is too deep.

Which of the following is a disadvantage of using decision trees for model selection?

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.

No concepts available.