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What is the main disadvantage of Decision Trees?

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Question: What is the main disadvantage of Decision Trees?

Options:

  1. They are computationally expensive
  2. They can easily overfit the training data
  3. They cannot handle missing values
  4. They require a large amount of data

Correct Answer: They can easily overfit the training data

Solution: 

Decision Trees can easily overfit the training data, especially if they are deep and complex.

What is the main disadvantage of Decision Trees?

Practice Questions

Q1
What is the main disadvantage of Decision Trees?
  1. They are computationally expensive
  2. They can easily overfit the training data
  3. They cannot handle missing values
  4. They require a large amount of data

Questions & Step-by-Step Solutions

What is the main disadvantage of Decision Trees?
  • Step 1: Understand what a Decision Tree is. It is a model used for making decisions based on data.
  • Step 2: Learn about overfitting. This happens when a model learns the training data too well, including its noise and outliers.
  • Step 3: Recognize that if a Decision Tree is too deep or complex, it can memorize the training data instead of generalizing from it.
  • Step 4: Realize that overfitting means the model may perform poorly on new, unseen data because it is too tailored to the training data.
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