The Matador

The Matador is a machine learning framework designed to detect and adapt to changes in data distributions over time, known as concept drift. It continuously monitors incoming data, assessing if the patterns it learned previously still apply. When it identifies a significant shift, it updates its models to maintain accuracy. Matador uses statistical techniques to decide when to relearn, ensuring it remains effective in dynamic environments like finance or cybersecurity. Its goal is to provide reliable, up-to-date predictions without the need for extensive retraining, making it useful for applications where data patterns evolve regularly.