How does Random Forest work? Why is it better than a single decision tree?
Use python machine learning.
Answer the following questions:
1. How does Random Forest work? Why is it better than a single decision tree?
2. Why is Random Forest better than a single decision tree? How does it decrease model error? How does it affect bias and virance?
3. What is Bagging?
Since you have asked multiple questions, we will solve the first question for you. If you want any specific question to be solved then please specify the question number or post only that question.
Random Forest is an ensemble learning method that generates multiple decision trees and combines their predictions to make a final classification or regression prediction. The idea behind Random Forest is to create multiple decision trees and let them vote to make a final prediction, rather than relying on a single decision tree, which can be prone to overfitting or underfitting the data.
Step by step
Solved in 3 steps
