Underfitting occurs when a model is too simple to capture patterns in the data, leading to poor accuracy both during training and in real use.
Why it matters:
- Produces weak models that miss important insights.
- Often caused by not enough training data or overly simplistic algorithms.
Pro tip: Balance is key — avoid models that are too simple or too complex.
« Back to Glossary Index