---
title: "Discussion on \"Logistic Regression from First Principles: Log-Odds, Likelihood and the Decision Threshold\" | Hashnode"
description: "Discussion on \"Logistic Regression from First Principles: Log-Odds, Likelihood and the Decision Threshold\". Logistic regression derived step by step: sigmoid and log-odds, log loss and its gradient, gradient descent and Newton's method, and the decision threshold."
url: "https://hashnode.com/posts/logistic-regression-from-first-principles/6af340fb4f49d259cfecfa43"
---

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# [Logistic Regression from First Principles: Log-Odds, Likelihood and the Decision Threshold](https://siddheshsawant.hashnode.dev/logistic-regression-from-first-principles?utm_source=hashnode&utm_medium=feed)

Logistic regression derived step by step: sigmoid and log-odds, log loss and its gradient, gradient descent and Newton's method, and the decision threshold.

23m ago · 53 min read · siddheshsawant.hashnode.dev  
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-   [#machine-learning](https://hashnode.com/tag/machine-learning)
-   [#logistic-regression](https://hashnode.com/tag/logistic-regression)
-   [#classification](https://hashnode.com/tag/classification)
-   [#optimization](https://hashnode.com/tag/optimization)
-   [#python](https://hashnode.com/tag/python)

## Written by

[Siddhesh Sawant](https://hashnode.com/@siddheshsawant)

@siddheshsawant · Mumbai, India

  
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Breaking down complex software engineering concepts with code and real-world examples

Backend Engineer with 5+ years of experience building and scaling high-performance microservices for large-scale e-commerce platforms. Have Expertise in Java, Spring Boot, Python and event-driven architectures, with proven ownership in designing Kafkabased systems, improving reliability and observability, and delivering high-impact customer-facing solutions

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