Author: Shub

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Data Preprocessing for Machine Learning in Auger

Generating a usable dataset for prediction and classification problems is usually the most time-consuming part of large data science problems. Most machine learning algorithms work only with well-structured data, generally tables with only numerical values. But most real-world data contains many exceptions to this requirement: missing values (e.g. which were not observed for some reason)

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Ensembles in Auger

Among the many advanced automated machine learning techniques which Auger offers, one is to provide better predictive results is ensemble generation. Ensemble generation is a powerful technique which aims to increase the predictive performance for a given machine learning task by combining several predictive models. It often improves the generalization error of the model and robustness to

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