csinva/mdl-complexity
MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".
This tool helps researchers and data scientists evaluate the generalization ability of their predictive models, especially when working with limited training data. It takes your training dataset and the model you've built, then computes a 'Minimum Description Length Complexity' score. This score helps you choose the best model by indicating how well it will perform on new, unseen data, often as effectively as cross-validation.
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Use this if you need a theoretically sound way to select the best predictive model for your task, especially when you have limited data and want to understand its potential to generalize.
Not ideal if you are looking for a simple, off-the-shelf hyperparameter tuning library without needing to understand underlying model complexity principles.
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Jun 12, 2023
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