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Bringing Foundation Models to Small Data
This article explores TabPFN, a transformer-based foundation model designed for small tabular datasets. Trained on millions of synthetic datasets generated via structural causal models, TabPFN learns to predict labels through in-context learning. It outperforms traditional methods like CatBoost and XGBoost in both speed and accuracy, while offering robustness, interpretability, and fine-tuning capabilities. A breakthrough in tabular ML, it redefines what's possible on structu

Juan Manuel Ortiz de Zarate
Apr 1111 min read
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Harnessing the Power of Bagging in Ensemble Learning
Boost your model's accuracy with bagging! Learn how ensemble techniques can stabilize predictions and improve performance.

Juan Manuel Ortiz de Zarate
Aug 7, 202410 min read
8 views


The Fundamental Tool in Machine Learning: Decision Trees
Unlock the power of decision trees! Discover how this simple yet robust tool can revolutionize your data analysis.

Juan Manuel Ortiz de Zarate
Jul 26, 202411 min read
23 views
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