Overfitting
AIGP glossary · Last reviewed: · By Victor Humenhuk (AIGP certified)
Overfitting - Model learns the training examples too specifically and performs weakly on new, unseen data.
In the AIGP body of knowledge, Overfitting comes up under Module 2: AI Impacts & Responsible AI · BoK II.A; Module 8: AI Governance Vocabulary.
Overfitting in context
- Know [[Implicit bias]], [[Sampling bias]] and [[Temporal bias]] on sight, plus the failure modes [[Overfitting]], [[Underfitting]] and [[Edge cases & outliers]] - and remember ==not all bias is harmful or illegal==. (Individual harms and the anatomy of bias)
- ==Variables live in the data; parameters and weights live in the model== - and overfitting (memorising) vs underfitting (too simple) is a classic confusion pair. (Model mechanics and performance)
Where Overfitting is covered in the AIGP study notes
Related terms
- Implicit bias
- Sampling bias
- Temporal bias
- Underfitting
- Edge cases & outliers
- Reidentification
- Appropriation
- Faulty inference
- Parameters
- Weights
Test yourself on Overfitting
Recognising a definition is not the same as applying it in an exam scenario. Work through the free AIGP practice questions, or read the full AIGP study guide - every study note is free.