Visual Diagnostics for More Effective Machine Learning
Description
Modeling is often treated as a search activity: find some combination of features, algorithm, and hyperparameters that yields the best score after cross-validation. In this talk, we will explore how to steer the model selection process with visual diagnostics and the Yellowbrick library, leading to more effective and more interpretable results and faster experimental workflows.