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This may revolutionize data science: we introduce TabPFN, a new tabular data classification method that takes 1 second & yields SOTA performance (better than hyperparameter-optimized gradient boosting in 1h). Current limits: up to 1k data points, 100 features, 10 classes. 🧵1/6 
TabPFN is radically different from previous ML methods. It is meta-learned to approximate Bayesian inference with a prior based on principles of causality and simplicity. Here‘s a qualitative comparison to some sklearn classifiers, showing very smooth uncertainty estimates. 2/6
TabPFN happens to be a transformer, but this is not your usual trees vs nets battle. Given a new data set, there is no costly gradient-based training. Rather, it’s a single forward pass of a fixed network: you feed in (Xtrain, ytrain, Xtest); the network outputs p(y_test). 3/6
