Blog
Self-published2023Principal Component Analysis, built from first principles in Python and NumPy and wrapped in a scikit-learn-style class. We work through the covariance matrix, eigendecomposition, and component selection on a toy dataset.
Self-published2023We implement K Nearest Neighbors from first principles in Python and NumPy as a reusable class object. Distance computation, neighbor selection, and majority voting are each built up and tested on a toy dataset.
Self-published2021This post adapts test-time augmentation, a computer vision staple, to tabular data. We benchmark strategies for numeric and categorical features and measure how much accuracy they actually buy at inference.
Self-published2020We forecast retail demand with gradient boosted trees in our Data Mining Cup 2020 solution. Optimizing a profit-based objective rather than plain forecast error aligns the model with the underlying inventory cost and revenue trade-offs.
Self-published2020This post introduces the fairness R package for computing and visualizing algorithmic fairness metrics. We walk through demographic parity, proportional parity, and equalized odds, and show how the three diverge on the very same classifier.