6. Ablation Studies

Finally, the gold standard in building complex machine learning models is proving that each constituent part of the model contributes something to the proposed solution.

Ablation studies serve to dissect machine learning models and evaluate their impact.

In this section, we’ll finally discuss how to present complex machine learning models in publications and ensure the viability of each part we engineered to solve our particular problem set.

Explore the Jupyter notebook on ablation study.

Here are some of the benefits taken from the motivation section.

6.1. Foster Collaboration

The guide on ablation studies works through an understanding of reducing model components.

This method builds trust in all the parts used to build a machine learning model to avoid spurious components that sneak in through the iterative nature of building data-driven solutions.