What is a primary advantage of using Random Forests over a single Decision Tree?

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Question: What is a primary advantage of using Random Forests over a single Decision Tree?

Options:

Correct Answer: Higher accuracy due to ensemble learning

Solution:

Random Forests combine multiple Decision Trees to improve accuracy and reduce overfitting, leveraging ensemble learning.

What is a primary advantage of using Random Forests over a single Decision Tree?

What is a primary advantage of using Random Forests over a single Decision Tree?

Step 1: Understand what a Decision Tree is. A Decision Tree is a model that makes decisions based on asking a series of questions.
Step 2: Recognize that a single Decision Tree can sometimes make mistakes, especially if it fits too closely to the training data (this is called overfitting).
Step 3: Learn that Random Forests use many Decision Trees instead of just one. This is called ensemble learning.
Step 4: Realize that by combining the results of multiple Decision Trees, Random Forests can make better predictions and are less likely to overfit.
Step 5: Conclude that the primary advantage of Random Forests is improved accuracy and reduced overfitting compared to a single Decision Tree.

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