Little Known Secrets about Interpretable Machine Learning on Synthetic Data
This first article in a new series on synthetic data and explainable AI, focuses on making linear regression more meaningful and controllable. Includes synthetic data, advanced machine learning with Excel, combinatorial feature selection, parametric bootstrap, cross-validation, and alternatives to R-squared to measure model performance. The full technical article (PDF, 13 pages, with detailed explanations and […]
Read More
Why are Confidence Regions Elliptic? Simple Explanation
A 90% confidence region is a domain of minimum area, containing 90% of the mass of a distribution. By distribution, here I mean a bivariate probability distribution, though the concept is not specific to machine learning. The 90% is called the confidence level, and I denote it as γ. Confidence regions are a generalization of […]
Read More
New Book: Stochastic Processes and Simulations
Introduction This scratch course on stochastic processes covers significantly more material than usually found in traditional books or classes. The approach is original: I introduce a new yet intuitive type of random structure called perturbed lattice or Poisson-binomial process, as the gateway to all the stochastic processes. Such models have started to gain considerable momentum […]
Read More
Advanced Machine Learning with Basic Excel – Part 1
It is amazing what you can do with a simple tool such as Excel. In this series, I share some of my spreadsheets. They cover many topics, including multiple types of regression, model-free confidence intervals, resampling, an original technique known as hidden decision trees, scatter plots with multiple groups, advanced visualization techniques, and more. No […]
Read More
You must be logged in to post a comment.