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

Discretization transforms continuous data into categories for better analysis. Explore key techniques for both unsupervised and supervised m

Boost your model's accuracy with bagging! Learn how ensemble techniques can stabilize predictions and improve performance.

Unlock the power of decision trees! Discover how this simple yet robust tool can revolutionize your data analysis.