Hyacehila's Blog

An introduction to MCP’s Host, Client, and Server layers, stdio and Streamable HTTP transports, and the workflow for building and debugging MCP servers and clients.

A discussion of why statistics drifted away from applied settings while pursuing theoretical completeness, and how data science and machine learning reconnect with learning from data.

A review of 60 years of data science, from John Tukey’s prediction to David Donoho’s broader view, discussing how statistics, computer science, and machine learning converged.

R-squared is not a simple model error function. Its definition hides a comparison against a constant model, so it never directly measures fit to the real world.

A repost of Jonas Kristoffer Lindelov’s classic article showing the unified linear-model logic behind t-tests, ANOVA, chi-square tests, and other common methods.

Are deep learning methods always effective for time-series forecasting? Based on Christoph Bergmeir’s NeurIPS 2024 talk, this post discusses model limits and evaluation traps.

PCR and PLSR are common dimensionality-reduction regression methods under multicollinearity. This post derives their logic and explains how PLSR brings in response relevance.

When data looks too perfect, it may have drifted away from reality. Starting from Benford’s Law, this post reveals statistical fingerprints hidden in fabricated data.

Every fall, Matt Might explains what a Ph.D. means to new doctoral students. This post uses that classic illustration to describe knowledge accumulation through doctoral research.

A summary of empirical scaling laws from Kaplan to Chinchilla, with a brief discussion of tree models and scaling laws.