What is the main disadvantage of using a Decision Tree?

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Question: What is the main disadvantage of using a Decision Tree?

Options:

Correct Answer: High variance

Solution:

Decision Trees are prone to high variance, meaning they can overfit the training data and perform poorly on unseen data.

What is the main disadvantage of using a Decision Tree?

What is the main disadvantage of using a Decision Tree?

Step 1: Understand what a Decision Tree is. It is a model used to make 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: Realize that when a Decision Tree overfits, it means it performs very well on the training data but poorly on new, unseen data.
Step 4: Recognize that high variance means the model's performance can change a lot with different training data.
Step 5: Conclude that the main disadvantage of using a Decision Tree is its tendency to overfit, leading to poor performance on new data.

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