Numerical Computing with Python

Comparison of error components across various styles of models

Errors need to be evaluated in order to measure the effectiveness of the model in order to improve the model's performance further by tuning various knobs. Error components consist of a bias component, variance component, and pure white noise:

Out of the following three regions:

• The first region has high bias and low variance error components. In this region, models are very robust in nature, such as linear regression or logistic regression.
• Whereas the third region has high variance and low bias error components, in this region models are very wiggly and vary greatly in nature, ...