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Submitted by Radford Neal
Bayesian neural networks, using various architectures, sometimes looking at principle components, with some special stuff for HIVA and NOVA. Models were chosen to be rather general, with selection beyond that based on validation performance and the model's own predicted performance on the test set. For HIVA, I ended up averaging predictions of three models.
|Dataset||Balanced Error||Test guess||Guess error||Test score||Area Under Curve|
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