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What is a disadvantage of using Decision Trees in real-world applications?

Practice Questions

Q1
What is a disadvantage of using Decision Trees in real-world applications?
  1. They are easy to interpret
  2. They can easily overfit the training data
  3. They require less computational power
  4. They handle missing values well

Questions & Step-by-Step Solutions

What is a disadvantage of using Decision Trees in real-world applications?
  • Step 1: Understand what a Decision Tree is. It is a model that makes decisions based on asking a series of questions about the data.
  • Step 2: Learn about overfitting. This happens when a model learns the training data too well, including its noise and outliers.
  • Step 3: Realize that if a Decision Tree is too complex, it can memorize the training data instead of generalizing from it.
  • Step 4: Recognize that overfitting leads to poor performance on new, unseen data because the model is too tailored to the training data.
  • Step 5: Conclude that one disadvantage of Decision Trees is their tendency to overfit, especially with complex datasets.
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