Welcome back! In this session, we're going to delve into the realm of Logistic Regression. Our primary materials for this exercise are Python, the Scikit-learn library, and the renowned Iris dataset. The end goal is the mastery of the theory and practical implementation of logistic regression for classification tasks and a solid comprehension of its utility and applications.
Logistic Regression is your go-to statistical model for binary classification tasks. For example, if you need to classify objects, such as distinguishing an apple from an orange based on features like color or size, logistic regression can do the job. Don't let the term 'regression' in its name mislead you, though. Unlike linear regression that predicts a continuous outcome, logistic regression works in the realm of probabilities, making it superb for dichotomous outcomes, like yes/no or true/false decisions.
The magic behind logistic regression is the sigmoid function, also known as the logistic function. It's a mathematical function that takes any real-valued number and squeezes it into a range between 0 and 1. In mathematical terms, the sigmoid function is defined as:
