How to Calculate the Bias-Variance Trade-off with Python

Last Updated on August 26, 2020

The performance of a machine learning model can be characterized in terms of the bias and the variance of the model.

A model with high bias makes strong assumptions about the form of the unknown underlying function that maps inputs to outputs in the dataset, such as linear regression. A model with high variance is highly dependent upon the specifics of the training dataset, such as unpruned decision trees. We desire models with low bias and low variance, although there is often a trade-off between these two concerns.

The bias-variance trade-off is a useful conceptualization for selecting and configuring models, although generally cannot be computed directly as it requires full knowledge of the problem domain, which we do not have. Nevertheless, in some cases, we can estimate the error of a model and divide the error down into bias and variance components, which may provide insight into a given model’s behavior.

In this tutorial, you will discover how to calculate the bias and variance for a machine learning model.

After completing this tutorial, you will know:

• Model error consists of model variance, model bias, and irreducible error.
• We seek models with low
  To finish reading, please visit source site