Category Archives: Machine Learning

One evening while I was walking my dogs, I came up with an idea for a simple data anomaly detection algorithm. I call it farthest centroid anomaly detection. The idea is very simple. If you have a source dataset that … Continue reading →

I wrote an article titled “Multi-Class Classification Using LightGBM” in the May 2024 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/Articles/2024/05/02/LightGBM-multi-class-classification.aspx. A multi-class classification problem is one where the goal is to predict a discrete variable that has three or … Continue reading →

One evening, while I was walking my two dogs, I thought about the possibility of looking for data anomalies by analyzing principal component analysis (PCA) reconstruction error. Bottom line: the technique works, but it just doesn’t feel right to me. … Continue reading →

The terms one-shot learning, few-shot learning, zero-shot learning, and fine-tuning don’t have universally agreed-upon definitions. All four terms are kinds of “transfer learning” where the goal is to start with an existing model and use it on a new problem. … Continue reading →

Time series regression (TSR) problems are very challenging. There are dozens of techniques — and the fact that there are so many techniques for TSR indicates that there’s no single best approach. There’s been quite a bit of recent research … Continue reading →

I’ve been looking at the LightGBM (light gradient boosting machine) system lately. One morning before work, I figured I’d zap out a regression demo. LightGBM is a sophisticated tree-based system that can perform classification (multi-class and binary), regression, and ranking. … Continue reading →

Data clustering is the process of grouping data items together so that similar items are in the same group/cluster. For strictly numeric data, the k-means clustering technique is simplest, and the most commonly used. For non-numeric, i.e. categorical data, there … Continue reading →

Early one Sunday morning, while I was waiting for the dog path to dry off from the evening rain so that I could walk my mutts, I figured I’d take a look at multi-class classification using the LightGBM (light gradient … Continue reading →

I wrote an article titled “Data Anomaly Detection Using a Neural Autoencoder with C#” in the April 2024 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/Articles/2024/04/15/data-anomaly-detection.aspx. Data anomaly detection is the process of examining a set of source data to … Continue reading →