AI engineering glossary · ML fundamentals
What is training error?
Training error (also called In-sample error): The average error a model makes on the same data it was fitted to. It is easy to make small by using a very flexible model, so on its own it says little about how the model will do on new cases.
Training error: an example
A tree that memorises 1,000 rows has 0% training error yet may make many mistakes on new rows.
Related terms
More ML fundamentals terms
- Training example
- Training set
- Transfer learning
- True risk
- Underfitting
- Unsupervised learning
- Validation curve
- Validation set
Where you learn it
Training 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.