Evaluating Model Generalizability
A well-performing model on training and validation sets doesn’t always generalize well to unseen data and even when it does when initially put into production, data changes over time, so today’s well-performing model is tomorrow’s failed predictor. Due to this inevitable degradation of model performance over time, machine learning operations, or MLOps, was created for continuously evaluating model performance over time and, when that performance decreases beyond an acceptable level, the model is retrained.
This recipe focuses on techniques to assess and ensure the generalization capability of machine learning models. We’ll explore learning curves, validation curves, and cross-validation strategies that help prevent overfitting and support robust model development.
Getting ready
We’ll use a synthetic classification dataset and logistic regression to demonstrate how model performance changes with different amounts of...
