Your Model Scored 98%. Should You Trust It?
How data leakage, flawed model selection, and inappropriate evaluation splits can produce misleading machine learning results—and how to avoid them.
Writing & Notes
Blog
Explorations in mathematics, probability, statistics, and data science. Ideas, derivations, experiments, and practical lessons.
Why Does PCA Work? A Geometric and Statistical Derivation
A step-by-step derivation of principal component analysis, connecting variance maximization, covariance eigenvectors, and optimal linear reconstruction.
Eigenvectors: From Linear Transformations to Principal Component Analysis
A mathematical journey from eigenvectors and covariance matrices to PCA, including the maximum-variance derivation, geometric projections, explained variance, and practical limitations.