Category Archives: JavaScript

Kernel ridge regression (KRR) is a technique to predict a single numeric value. KRR uses a kernel function, which compares two vectors and computes a measure of their similarity, in order to handle complex non-linear data. KRR uses the ridge … Continue reading →

I wrote an article titled “Anomaly Detection Using K-Means Clustering with JavaScript” in the December 2025 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2025/12/01/anomaly-detection-using-k-means-clustering-with-javascript.aspx. Anomaly detection is the process of examining a dataset to find data items that are unusual … Continue reading →

Anomaly detection is the process of examining a dataset to find data items that are unusual in some way. There are many different anomaly detection techniques. One of the basic techniques is simple: use k-means clustering to group the dataset … Continue reading →

One morning before work, I figured I’d revisit my JavaScript implementation of k-means data clustering. For my demo, I used a set of synthetic data that looks like: -0.1660 0.4406 -0.9998 -0.3953 -0.7065 0.4840 0.0776 -0.1616 0.3704 -0.5911 0.7562 0.1568 … Continue reading →

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = 0, female = 1) based … Continue reading →

I wrote an article titled “Linear Regression with Two-Way Interactions Using JavaScript” in the October 2025 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2025/10/01/linear-regression-with-two-way-interactions-using-javascript.aspx. The goal of a machine learning regression problem is to predict a single numeric value. The … Continue reading →

Summary: I used the “pdf-to-img” JavaScript library/package to convert a PDF file to an image, but the library has too many deprecated sub-libraries for pdf-to-img to be useful in anything other than a demo scenario. Not every exploration turns out … Continue reading →

I like to write some code every morning before work. One Tuesday morning I decided to add a function that computes the coefficient of determination (aka R2) to my standard JavaScript implementation of kernel ridge regression. There are about a … Continue reading →

When I was a college student, I remember studying linear regression with two-way interactions. It’s a valuable technique, but one which is rarely used nowadays. The goal of a machine learning regression problem is to predict a single numeric value. … Continue reading →