How does a Random Forest improve upon a single Decision Tree?
Practice Questions
1 question
Q1
How does a Random Forest improve upon a single Decision Tree?
By using a single model for predictions
By averaging the predictions of multiple trees
By increasing the depth of each tree
By using only the most important features
Random Forest improves accuracy by averaging the predictions of multiple Decision Trees, which reduces variance.
Questions & Step-by-step Solutions
1 item
Q
Q: How does a Random Forest improve upon a single Decision Tree?
Solution: Random Forest improves accuracy by averaging the predictions of multiple Decision Trees, which reduces variance.
Steps: 7
Step 1: Understand that a Decision Tree is a model that makes predictions based on a series of questions about the data.
Step 2: Realize that a single Decision Tree can be very sensitive to the data it is trained on, which can lead to overfitting (making it too specific to the training data).
Step 3: Learn that a Random Forest is made up of many Decision Trees, not just one.
Step 4: Know that each tree in the Random Forest is trained on a different random sample of the data.
Step 5: Understand that when making a prediction, the Random Forest takes the average of the predictions from all the trees.
Step 6: Recognize that averaging the predictions helps to smooth out the errors from individual trees, leading to better overall accuracy.
Step 7: Conclude that by using multiple trees and averaging their predictions, Random Forest reduces the risk of overfitting and improves the model's performance.
