In Random Forests, what does the term 'out-of-bag error' refer to?
Practice Questions
1 question
Q1
In Random Forests, what does the term 'out-of-bag error' refer to?
Error on the training set
Error on unseen data
Error calculated from the samples not used in training a tree
Error from the final ensemble model
Out-of-bag error is an estimate of the model's performance calculated using the data points that were not included in the bootstrap sample for each tree.
Questions & Step-by-step Solutions
1 item
Q
Q: In Random Forests, what does the term 'out-of-bag error' refer to?
Solution: Out-of-bag error is an estimate of the model's performance calculated using the data points that were not included in the bootstrap sample for each tree.
Steps: 6
Step 1: Understand that Random Forests is a machine learning method that uses many decision trees to make predictions.
Step 2: Know that each decision tree in a Random Forest is built using a random sample of the data, called a bootstrap sample.
Step 3: Realize that when a bootstrap sample is created, some data points are left out. These left-out data points are called 'out-of-bag' (OOB) samples.
Step 4: For each tree in the Random Forest, the out-of-bag samples can be used to test how well that tree performs.
Step 5: The out-of-bag error is calculated by checking how many of these left-out samples were incorrectly predicted by the tree.
Step 6: The overall out-of-bag error for the Random Forest is the average error across all trees, using their respective out-of-bag samples.
