Supervised domain-agnostic prediction framework for probabilistic modelling
A supervised domain-agnostic framework that allows for probabilistic modelling, namely the prediction of probability distributions for individual data points. The package offers a variety of features and specifically allows for the implementation of probabilistic prediction strategies in the supervised contexts comparison of frequentist and Bayesian prediction methods strategy optimization through hyperparamter tuning and ensemble methods (e.g. bagging) workflow automation List of developers and contributors Documentation The full documentation is available here. Installation Installation is easy using Python’s package manager $ […]
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