AI engineering glossary · Classical ML algorithms
What is out-of-bag error?
Out-of-bag error (also called OOB error): An error estimate for bagged models that uses the rows each tree did not see in its bootstrap sample. Every row is predicted only by trees that left it out, so the score behaves like validation data without setting any data aside.
Out-of-bag error: an example
Setting oob_score=True in RandomForestClassifier reports 0.86 accuracy measured on the left-out rows of each tree.
Related terms
More Classical ML algorithms terms
- Perceptron
- Permutation importance
- Polynomial kernel
- Polynomial regression
- Principal component
- Principal component analysis
- Quadratic discriminant analysis
- Random forest
Where you learn it
Out-of-bag error is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.