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Why CatBoost Works So Well: The Engineering Behind the Magic | Towards Data Science
CatBoost stands out by directly tackling a long-standing challenge in gradient boosting—how to handle categorical variables effectively without causing target leakage. By introducing innovative techniques such as Ordered Target Statistics and Ordered Boosting, and by leveraging the structure of Oblivious Trees, CatBoost efficiently balances robustness and accuracy. These methods ensure that each prediction uses only past data, preventing leakage and resulting in a model that is both fast and reliable for real-world tasks.
A Little More Conversation, A Little Less Action — A Case Against Premature Data Integration | Towards Data Science
Running a large data integration project before embarking on the ML part is easily a bad idea, because you integrate data without knowing its use . The chances that the data is going to be fit for purpose in some future ML use case is slim, at best This article shows how having people talking together helps avoid the trap of premature data integration in ML projects, optimising value for money.