When a data science problem is "the" problem
The post Ivory Tower Notes: The Problem appeared first on Towards Data Science.

Inside Deb8flow: Real-time AI debates with LangGraph and GPT-4o
The post Deb8flow: Orchestrating Autonomous AI Debates with LangGraph and GPT-4o appeared first on 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.
The post Why CatBoost Works So Well: The Engineering Behind the Magic appeared first on Towards Data Science.

Learn the intuition behind time series decomposition, additive vs. multiplicative models and build your first forecasting baseline model using Python
The post Time Series Forecasting Made Simple (Part 1): Decomposition and Baseline Models appeared first on Towards Data Science.

Using decision trees for quick segmentation
The post Mining Rules from Data appeared first on Towards Data Science.

How to enable your ML model to run anywhere
The post A Data Scientist’s Guide to Docker Containers appeared first on Towards Data Science.

Go beyond the definitions: grasp the real meaning of AUC and ROC analysis for practical data science
The post Unlock the Power of ROC Curves: Intuitive Insights for Better Model Evaluation appeared first on Towards Data Science.

Reverse-engineering large languages models' computation circuit to understand their decision-making processes
The post Circuit Tracing: A Step Closer to Understanding Large Language Models appeared first on Towards Data Science.