What is the role of 'bootstrap sampling' in Random Forests?
Practice Questions
Q1
What is the role of 'bootstrap sampling' in Random Forests?
To select features for each tree
To create multiple subsets of the training data
To evaluate model performance
To increase the depth of trees
Questions & Step-by-Step Solutions
What is the role of 'bootstrap sampling' in Random Forests?
Step 1: Understand that Random Forests is a machine learning method that uses many decision trees to make predictions.
Step 2: Learn that 'bootstrap sampling' is a technique used to create different sets of data from the original training data.
Step 3: Realize that in bootstrap sampling, we take random samples from the training data, but we allow the same data point to be chosen more than once (this is called 'sampling with replacement').
Step 4: Know that each subset created through bootstrap sampling is used to train a different decision tree in the Random Forest.
Step 5: Understand that by using different subsets of data, each tree can learn different patterns, making the overall model more robust and accurate.
