Author Archives: jamesdmccaffrey

First, the title of this blog post is moderately click-bait. I dislike many charateristics of XGBoost but I like some of them too. XGBoost (“extreme gradient boost”) is a huge library of many functions, with hundreds of parameters and possible … Continue reading →

Imbalanced data occurs when you have machine learning training data with many items of one class and very few items of the other class. For example, some medical data might have many thousands of data items that are “no disease” … Continue reading →

I contributed to an article titled “Researchers Explore Intelligent Sampling of Huge ML Datasets to Reduce Costs and Maintain Model Fairness” in the May 2021 edition of the online Pure AI site. See https://pureai.com/articles/2021/05/03/intelligent-ai-sampling.aspx. Researchers devised a new technique to … Continue reading →

I was walking my two dogs, Riley and Kevin, early one wet Pacific Northwest Saturday morning. I enjoy walking and thinking while my dogs do their dog-thing and look for rabbits. My dogs have never caught a rabbit but they’re … Continue reading →

I don’t use tree data structures very often. When I expect to use a tree, I usually refresh my memory by coding a minimal example — coding trees is tricky and a warm-up example saves time in the long run. … Continue reading →

A variational autonencoder (VAE) is a deep neural system that can generate synthetic data items. One possible use of a VAE is to generate synthetic minority class items (those with very few instances) for an imbalanced dataset. At least in … Continue reading →

A few days ago I did some thought experiments about different schemes to automatically stop training a logistic regression model. I was motivated by the poor performance of the sckit library LogisticRegression model with default parameters. I coded up a … Continue reading →

I often think about new machine learning ideas in two different, but related ways. One approach is to look at a specific, practical problem and then mentally examine my collection of ML techniques to see if I have a way … Continue reading →

Suppose you have a binary classification problem where there are many of one class, but very few of the other class. For example, with medical data, you might have many thousands of data items representing people, that are class 0 … Continue reading →