Tabular data—structured information stored in rows and columns—is at the heart of most real-world machine learning problems, from healthcare records to financial transactions. Over the years, models based on decision trees, such as Random Forest, XGBoost, and CatBoost, have become the default choice for these tasks. Their strength lies in handling mixed data types, capturing […] The post How TabPFN Leverages In-Context Learning to Achieve Superior Accuracy on Tabular Datasets Compared to Random Forest and CatBoost appeared first on MarkTechPost.