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What is the main disadvantage of Decision Trees?
What is the main disadvantage of Decision Trees?
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Practice Questions
1 question
Q1
What is the main disadvantage of Decision Trees?
They are computationally expensive
They can easily overfit the training data
They cannot handle missing values
They require a large amount of data
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Decision Trees can easily overfit the training data, especially if they are deep and complex.
Questions & Step-by-step Solutions
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Q
Q: What is the main disadvantage of Decision Trees?
Solution:
Decision Trees can easily overfit the training data, especially if they are deep and complex.
Steps: 4
Show Steps
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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