Revisiting Logistic Regression — A Gentle Introduction to Generalized Linear Models
Brush Up on Your Foundational Machine Learning and Statistics Concepts With an Intuitive, Yet Rigorous Explanation of Logistic Regression and Generalized Linear Models.
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All models are wrong, but some are useful
The two fundamental pillars of supervised statistical learning — Regression and Classification. Simple Linear Regression and Logistic Regression is how many of us have started our journey in Statistics and Data Science. A long-standing debate still prevails on why is Logistic Regression a Classification Model instead of a Regression Model?
Here we revisit Logistic Regression from an intuitive perspective along with statistical rigor. We will briefly touch up on the concepts behind Generalized Linear Model along with an optional section on Iterative Re-weighted Least Squares (IRLS) to fit these models.
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Rishi Dey Chowdhury
Master of Statistics
My research interests include Artificial Intelligence, Quant Trading, Deep Learning and their applications in Market Microstructure, Computer Vision and NLP.