Category Archives: JavaScript

The goal of a machine learning regression problem is to predict a single numeric value. For example, you might want to predict the bank account balance of a person based on his annual income, age, years of education, and so … Continue reading →

The form of a linear regression prediction equation is y’ = (w0 * x0) + (w1 * x1) + . . + (wn * xn) + b where y’ is the predicted value, the xi are predictor values, the wi … Continue reading →

I wrote an article titled “Kernel Ridge Regression with Cholesky Inverse Training Using JavaScript” in the January 2026 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2026/01/06/kernel-ridge-regression-with-cholesky-inverse-training-using-javascript.aspx. There are approximately a dozen common regression techniques. Examples include linear regression, nearest neighbors … Continue reading →

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 →