I am copying a pattern from Simon Willison by also having Claude Code write me some notebooks on occasion about algorithm-peformance things that I'm curious about.

Vincent D. Warmerdam

GridSearchCV in scikit-learn can be significantly faster for specific use cases by avoiding joblib/pickle serialization overhead. For Ridge regression, leveraging closed-form solutions allows computing optimal weights for all regularization parameters in one pass instead of looping. For Logistic regression, warm-starting subsequent models with previous weights provides massive speedups. While scikit-learn solves general problems, custom implementations for specific algorithms can achieve better performance. The author used Claude to generate benchmark notebooks, though LLM output still requires careful validation.

specific gridsearch