A simple yet effective model for dealing with linear classification problems.

<p>Logistic Regression is a cornerstone of machine learning, facilitating classification by modeling the probability that an instance belongs to a particular category. The&nbsp;<code>scikit-learn</code>&nbsp;(often abbreviated to&nbsp;<code>sklearn</code>) library in Python provides robust tools for building Logistic Regression models. This article will provide a detailed overview of the Logistic Regression model in&nbsp;<code>scikit-learn</code>, including its usage, attributes, methods, and practical tips.</p> <h2>What is Logistic Regression</h2> <p>Logistic Regression is a statistical method for predicting binary outcomes. Given a set of features, it estimates the probability that an instance/observation belongs to a particular category. Despite the name, it is used for classification problems, not regression problems. The model outputs a probability that the given input point belongs to a particular class, which is transformed into a binary outcome via a threshold (e.g., if the output probability is greater than 0.5, classify it as class 1, class 0).</p> <p><a href="https://medium.com/@deryl.baharudin/a-simple-yet-effective-model-for-dealing-with-linear-classification-problems-28d20018c7b7">Website</a></p>