What is the main advantage of using ensemble methods like Random Forest over a s
Practice Questions
Q1
What is the main advantage of using ensemble methods like Random Forest over a single decision tree?
They are faster to train
They reduce variance and improve prediction accuracy
They are easier to interpret
They require less data
Questions & Step-by-Step Solutions
What is the main advantage of using ensemble methods like Random Forest 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 about the data.
Step 2: Recognize that a single decision tree can be sensitive to the data it is trained on. This means it can make mistakes if the data is not perfect.
Step 3: Learn about ensemble methods. These methods combine multiple models to make better predictions.
Step 4: Know that Random Forest is an ensemble method that uses many decision trees instead of just one.
Step 5: Understand that by averaging the predictions of many decision trees, Random Forest reduces the chances of making errors that a single tree might make.
Step 6: Conclude that the main advantage of using Random Forest is that it improves prediction accuracy by reducing errors through averaging.
