What is a key characteristic of Random Forests compared to a single Decision Tree?
Practice Questions
1 question
Q1
What is a key characteristic of Random Forests compared to a single Decision Tree?
They are less prone to overfitting.
They require more computational resources.
They can only handle binary classification.
They are always more interpretable.
Random Forests are less prone to overfitting because they aggregate the predictions of multiple trees.
Questions & Step-by-step Solutions
1 item
Q
Q: What is a key characteristic of Random Forests compared to a single Decision Tree?
Solution: Random Forests are less prone to overfitting because they aggregate the predictions of multiple trees.
Steps: 5
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.
