Logistic regression is a powerful and widely used algorithm in machine learning, particularly for binary classification problems. Despite its name, it is not a regression model in the traditional sense. Instead, it is used to predict the probability of one of two possible outcomes, such as “yes” vs. “no” or “spam” vs. “not spam.” In this article, we’ll break down what logistic regression is, how it works, and why it is a go-to tool for many data scientists. What is Logistic Regression? At its core, it models the relationship between a set of independent variables (also known as features) and a dependent binary outcome . Unlike linear regression, which predicts continuous values, logistic regression outputs probabilities, making it suitable for classification tasks. The key function used in this model is the logistic function (also called the sigmoid function), which maps any real-valued number into a probability between 0 and 1. How Does it Wo...
Comments
Post a Comment