https://ryanbalshaw.github.io/sjmelck_pages/tags/logistic-regression/Recent content in Logistic Regression on SjmelckHugoen-usWed, 16 Sep 2026 10:00:05 +0200https://ryanbalshaw.github.io/sjmelck_pages/blog/logistic-regression/Wed, 23 Apr 2025 15:51:58 +0200https://ryanbalshaw.github.io/sjmelck_pages/blog/logistic-regression/<h2 id="introduction"> Introduction <a class="heading-anchor" href="#introduction" data-copy-heading aria-label="Copy link to Introduction"> <i class="bi bi-link-45deg" aria-hidden="true"></i> </a> </h2> <p>Good day! &#x1f44b;</p> <p>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.</p>