How do Random Forests improve prediction accuracy?
Practice Questions
Q1
How do Random Forests improve prediction accuracy?
By using a single Decision Tree
By averaging predictions from multiple trees
By reducing the number of features
By increasing the depth of trees
Questions & Step-by-Step Solutions
How do Random Forests improve prediction accuracy?
Step 1: Understand that a Decision Tree is a model that makes predictions based on data features.
Step 2: Realize that a single Decision Tree can make mistakes and may not always be accurate.
Step 3: Learn that a Random Forest is made up of many Decision Trees working together.
Step 4: Know that each tree in the Random Forest makes its own prediction based on the data.
Step 5: Understand that the Random Forest takes the average of all the predictions from the trees.
Step 6: Recognize that averaging helps to reduce errors and improve overall accuracy.
Ensemble Learning – Random Forests utilize ensemble learning by combining multiple Decision Trees to enhance prediction accuracy.
Overfitting Reduction – By averaging predictions from various trees, Random Forests reduce the risk of overfitting that can occur with individual Decision Trees.
Bootstrap Aggregating (Bagging) – Random Forests employ bagging, where each tree is trained on a random subset of the data, contributing to improved robustness and accuracy.
