Author Archives: jamesdmccaffrey

I contributed some technical content and opinions to an article titled “AI Hallucinations: Why AI Confidently Lies and the Business Opportunities It Creates” on the Pure AI web site. See https://pureai.com/articles/2026/08/03/ai-hallucinations-why-ai-confidently-lies-and-the-business-opportunities-it-creates.aspx. An AI hallucination occurs when a generative AI model … Continue reading →

A simple decision tree for regression will almost always overfit the training data where the model accuracy on the training data is near 100% but accuracy on new, previously unseen data is very poor. The simple idea of random forest … Continue reading →

The goal of a machine learning regression problem is to predict a single numeric value. Quadratic regression is an enhanced form of basic linear regression. One of several ways to train a quadratic regression model is to use left pseudo-inverse … Continue reading →

My standard from-scratch implementation of kernel ridge regression (KRR) trains uses the explicit Cholesky inverse of a RBF kernel matrix. It’s possible to train by computing a Cholesky decomposition and then using a solve method. The explicit inverse approach looks … Continue reading →

I wrote an article titled “Computing Dataset Variance Inflation Factor (VIF) Using C#” in the August 2026 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2026/08/03/computing-dataset-variance-inflation-factor-vif-using-csharp.aspx. The variance inflation factor (VIF) of a dataset column is a value that measures the … Continue reading →

A simple decision tree for regression will almost always overfit the training data where the model accuracy on the training data is near 100% but accuracy on new, previously unseen data is very poor. The simple idea of bagging tree … Continue reading →

I recently refactored my basic kernel support vector regression system, implemented from scratch, using Python. I tested my refactored code using synthetic data, but I figured I should run the system on some real world data. One of the standard … Continue reading →

I recently (yesterday) made major revisions to my personal C# matrix QR decomposition using the Householder algorithm. In my work environment, I use matrix QR decomposition for matrix pseudo-inverse of a matrix of training data. So, my next step after … Continue reading →