![Model complexity and error: Bias-Variance Tradeoff](https://firebasestorage.googleapis.com/v0/b/onecademy-1.appspot.com/o/UploadedImages%2Fhamza-baccouche_Sun%2C%2023%20Feb%202020%2020%3A10%3A16%20GMT.png?alt=media&token=221775a1-9e19-4010-b3cc-505b93b0b039) 

University of Michigan - Ann Arbor

When selecting $K$ for K-Fold CV, we should consider a bias-variance trade-off. K-Fold CV provides $K$ estimates of the model error. The mean of the errors indicates the model's bias. A lower mean value indicates a higher accuracy of the model. The model variance is calculated based on the standard deviation of the errors. A lower variance indicates a low variation in the model performance. Usually, it is difficult to reduce both model bias and variance.

K-Fold Cross-Validation Bias-Variance Tradeoff

Understanding the Bias-Variance Tradeoff.  (2012, June). Retrieved from http://scott.fortmann-roe.com/docs/BiasVariance.html

Understanding the Bias-Variance Tradeoff

Model complexity and error: Bias-Variance Tradeoff

![Graphical illustration of Bias-Variance Tradeoff](https://firebasestorage.googleapis.com/v0/b/onecademy-1.appspot.com/o/UploadedImages%2Fhamza-baccouche_Sun%2C%2023%20Feb%202020%2020%3A11%3A41%20GMT.png?alt=media&token=5c0bfafc-b69e-4af7-ac10-783f9a1acfe8)


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