Underfitting and Overfitting

Explaining and solving bad models | The Data Series | Episode 5

Underfitting and overfitting are both common problems data scientists come across when evaluating their model. It is important you are aware of these issues and what we can do resolve them.

Definitions

Underfitting: Occurs when our model fails to capture the underlying trend in our data:

Models which underfit our data:

• Have a Low Variance and a High Bias
• Tend to have less features [ 𝑥 ]
• High-Bias: Assumes more about the form or trend our data takes
• Low Variance: Changes to our data makes small changes to our model’s predicted values

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Overfitting: Occurs when our model captures the underlying trend, however, includes too much noise and fails to capture the general trend: