Category Archives: JavaScript

Decision tree regression is a machine learning technique that incorporates a set of if-then rules in a tree data structure to predict a single numeric value. For example, a decision tree regression model prediction might be, “If employee age is … Continue reading →

Decision tree regression is a machine learning technique that incorporates a set of if-then rules in a tree data structure to predict a single numeric value. For example, a decision tree regression model prediction might be, “If employee age is … Continue reading →

I wrote an article titled “Quadratic Regression with SGD Training Using JavaScript” in the March 2026 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2026/03/11/quadratic-regression-with-sgd-training-using-javascript.aspx. The goal of a machine learning regression problem is to predict a single numeric value. For … Continue reading →

Bottom line: I implemented SVD using the Householder + QR algorithm, with JavaScript. The implementation works but it’s not as stable as my implementation that uses the Jacobi algorithm. In other words, the code in this post is interesting, but … Continue reading →

The goal of machine learning regression problem is to predict a single numeric value. For example, you might want to predict an employee’s salary based on age, height, high school grade point average, and so on. There are approximately a … Continue reading →

I wrote an article titled “Linear Regression with Pseudo-Inverse Training Using JavaScript” in the February 2026 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2026/02/02/linear-regression-with-pseudo-inverse-training-using-javascript.aspx. The goal of a machine learning regression problem is to predict a single numeric value. For … Continue reading →

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 →