AutoML is one of the most robust areas of innovation in applied machine learning. New products in this space from the likes of Google and new AI-focused startups (such as our own Auger.AI) are appearing constantly, all of which promise to make machine learning accessible to the masses without the need for trained data scientists. At
Read MoreAuger vs. H2O vs. TPOT On Sample Datasets We often get asked how Auger compares to other AutoML tools. Luckily in these days of open source tools this is possible to do in a way that can be validated and reproduced by other users. First let’s describe the choice of datasets. It was important that they
Read MoreGenerating 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)
Read MoreAmong 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
Read More