Logistic Regression Predicts Binary Classification with Probabilities with Machine learning

Abstract

Logistic regression is a statistical and machine learning technique used for binary classification problems, where the goal is to predict one of two possible outcomes. Unlike linear regression, which predicts continuous values, logistic regression predicts probabilities that are transformed into class labels (0 or 1) using a logistic function. This model is widely employed in various fields, such as finance for fraud detection, healthcare for disease diagnosis, and marketing for customer churn prediction. At the heart of logistic regression is the logistic function (also known as the sigmoid function), which maps the predicted linear combination of features to a value between 0 and 1, representing the probability of a particular class. The model learns the parameters of the logistic function using a technique called maximum likelihood estimation (MLE).This paper aims to provide an in-depth analysis of logistic regression, exploring its theoretical foundations, applications, and challenges. We will review the literature on its development, variations, and the scenarios in which logistic regression is particularly effective. Furthermore, the methodology section will guide through the steps of implementing logistic regression, from data preprocessing to model evaluation. Despite its simplicity, logistic regression can be prone to overfitting and is sensitive to the choice of features. The paper will also discuss strategies for mitigating these issues, such as regularization and feature selection. The conclusion will highlight the importance of logistic regression in machine learning, emphasizing its continued relevance despite the rise of more complex models.