What is a key characteristic of Random Forests compared to a single Decision Tre

₹0.0

Question: What is a key characteristic of Random Forests compared to a single Decision Tree?

Options:

Correct Answer: They are less prone to overfitting.

Solution:

Random Forests are less prone to overfitting because they aggregate the predictions of multiple trees.

What is a key characteristic of Random Forests compared to a single Decision Tree?

What is a key characteristic of Random Forests compared to 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 about the data.
Step 2: Recognize that a single Decision Tree can sometimes make mistakes by fitting too closely to the training data, which is called overfitting.
Step 3: Learn that Random Forests use many Decision Trees instead of just one. This means they create a 'forest' of trees.
Step 4: Understand that Random Forests combine the predictions from all the trees to make a final decision. This helps to balance out the mistakes of individual trees.
Step 5: Conclude that because Random Forests use multiple trees and average their predictions, they are less likely to overfit compared to a single Decision Tree.

Random Forests vs. Decision Trees – Random Forests combine multiple decision trees to improve prediction accuracy and reduce overfitting.