How does a Random Forest improve upon a single Decision Tree?

₹0.0

Question: How does a Random Forest improve upon a single Decision Tree?

Options:

Correct Answer: By averaging the predictions of multiple trees

Solution:

Random Forest improves accuracy by averaging the predictions of multiple Decision Trees, which reduces variance.

How does a Random Forest improve upon a single Decision Tree?

How does a Random Forest improve upon a single Decision Tree?

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.

Ensemble Learning – Random Forest is an ensemble method that combines multiple Decision Trees to improve predictive performance.
Variance Reduction – By averaging the predictions of multiple trees, Random Forest reduces the model's variance compared to a single Decision Tree.
Overfitting – Random Forest mitigates overfitting, which is a common issue with single Decision Trees.