Author Archives: jamesdmccaffrey

NFL 2026 Week 2 Predictions – Zoltar Likes the Underdog Falcons Against the Panthers

Posted on September 16, 2026 by jamesdmccaffrey

Zoltar is my NFL football prediction computer program. It uses a neural network and a type of (quasi) reinforcement learning. Here are Zoltar’s predictions for week #2 of the 2026 season. These predictions are fuzzy, in the sense that it … Continue reading →

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I Use AI to Improve My Decision Tree Regression Using From-Scratch Python

Posted on September 15, 2026 by jamesdmccaffrey

I fed my decision tree regression system, implemented using from-scratch Python with NumPy, to several AI systems and asked the AI to analyze it for correctness. I was quite impressed that the AI found a few rare edge cases where … Continue reading →

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Implementing a Coefficient of Determination (R2) Function Using C#

Posted on September 14, 2026 by jamesdmccaffrey

The goal of a machine learning regression problem is to predict a single numeric value, for example, predicting the bank account balance of a person based on his age, annual income, and so on. A prediction model can be evaluated … Continue reading →

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AdaBoost.R2 Regression with Blind Tree Learners Using C#

Posted on September 11, 2026 by jamesdmccaffrey

I decided to implement Gradient Boost regression using Blind Trees learners. Bottom line: For my demo dataset, the technique worked about the same as the standard architecture that uses regular decision trees as the learners. In machine learning, explaining what … Continue reading →

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“Quadratic Regression with QR-Householder OLS Solve Training Using C#” in Visual Studio Magazine

Posted on September 10, 2026 by jamesdmccaffrey

I wrote an article titled “Quadratic Regression with QR-Householder OLS Solve Training Using C#” in the September 2026 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2026/09/01/quadratic-regression-with-qr-householder-ols-solve-training-using-csharp.aspx. The goal of a machine learning regression problem is to predict a single numeric … Continue reading →

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Example of Kernel Ridge Regression From Scratch Python Using Cholesky Decomposition with a Solve Function

Posted on September 9, 2026 by jamesdmccaffrey

My standard from-scratch implementation of kernel ridge regression (KRR) trains using the 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 like: … Continue reading →

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NFL 2026 Week 1 Predictions – Zoltar Likes Underdogs Colts, Texans, Broncos

Posted on September 8, 2026 by jamesdmccaffrey

Zoltar is my NFL football prediction computer program. It uses a neural network and a form of quasi-reinforcement learning. Here are Zoltar’s predictions for week #1 of the 2026 season (which starts tomorrow, Wednesday, Sept. 9). These predictions are tentative, … Continue reading →

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Adding L2 Regularization to Quadratic Regression Trained Using MP Pseudo-Inverse via QR-Householder with C#

Posted on September 7, 2026 by jamesdmccaffrey

There are three main ways to train a quadratic regression model: 1.) using stochastic gradient descent, 2.) using left pseudo-inverse (normal equations) via Cholesky inverse, 3.) using relaxed Moore-Penrose pseudo-inverse via one of many possible inverses. I have implemented many … Continue reading →

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Comparing Support Vector Regression Using From-Scratch C# vs Using the Scikit SVR Module

Posted on September 4, 2026 by jamesdmccaffrey

I recently implemented support vector regression (SVR) from scratch using the C# language, trained using stochastic sub-gradient descent (SSGD). The SVR system seemed to work well but I figured I’d compare it with the gold standard scikit-learn SVR module. Bottom … Continue reading →

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AdaBoost.R2 Regression with Extra Trees (Extremely Randomized Trees) Learners Using C#

Posted on September 3, 2026 by jamesdmccaffrey

Bottom line: I decided to implement AdaBoost.R2 regression using Extra (“Extremely Randomized”) Trees learners. Bottom line: For my demo dataset, the technique worked better than the standard architecture that uses regular decision trees as the learners. As is often the … Continue reading →

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