In a Random Forest, what is the purpose of bootstrapping?
Practice Questions
1 question
Q1
In a Random Forest, what is the purpose of bootstrapping?
To reduce overfitting
To increase the number of features
To create multiple subsets of data for training
To improve model interpretability
Bootstrapping involves creating multiple subsets of the training data, which helps in building diverse trees in the Random Forest.
Questions & Step-by-step Solutions
1 item
Q
Q: In a Random Forest, what is the purpose of bootstrapping?
Solution: Bootstrapping involves creating multiple subsets of the training data, which helps in building diverse trees in the Random Forest.
Steps: 5
Step 1: Understand that Random Forest is a machine learning method that uses many decision trees to make predictions.
Step 2: Know that bootstrapping is a technique used to create different samples from the original training data.
Step 3: Realize that each sample is created by randomly selecting data points from the original dataset, allowing some points to be chosen multiple times while others may not be chosen at all.
Step 4: Understand that by creating these different samples, we can build different decision trees, which helps the Random Forest to learn from various perspectives.
Step 5: Recognize that having diverse trees improves the overall performance and accuracy of the Random Forest model.
