Logistic Regression from the ground up

Wed, 23 Apr 2025 15:51:58 +0200

Introduction

Good day! 👋

In this post, the logistic regression model is covered in detail. This model is used to predict binary outcomes and is well known for its ability to drive interpretability. The objective is to start with the basics of the model formulation, consider how to interpret the model parameters, introduce the concept of how to regularise the model, give the analytical forms of the gradient vector and Hessian matrix, and define how to extend the class to the multi-class classification form.

An introduction to Radial basis functions

Fri, 15 Mar 2024 11:59:05 +0200

Good day 👋

🧠 This is the first in a series of blog posts that deal with radial basis function surrogate models 🧑🏽‍🏫. The end goal will be to implement gradient enhanced models that complete powerful transformation procedures, but, we must first understand and implement the simplest version of these models.

Python on Sjmelck

Logistic Regression from the ground up

Introduction

An introduction to Radial basis functions