What is a primary advantage of using Random Forests over a single Decision Tree?
Practice Questions
Q1
What is a primary advantage of using Random Forests over a single Decision Tree?
Lower computational cost
Higher accuracy due to ensemble learning
Easier to interpret
Requires less data
Questions & Step-by-Step Solutions
What is a primary advantage of using Random Forests 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.
Step 2: Recognize that a single Decision Tree can sometimes make mistakes, especially if it fits too closely to the training data (this is called overfitting).
Step 3: Learn that Random Forests use many Decision Trees instead of just one. This is called ensemble learning.
Step 4: Realize that by combining the results of multiple Decision Trees, Random Forests can make better predictions and are less likely to overfit.
Step 5: Conclude that the primary advantage of Random Forests is improved accuracy and reduced overfitting compared to a single Decision Tree.
