AutoML ...

provides methods and processes to make Machine Learning available for non-Machine Learning experts, to improve efficiency of Machine Learning and to accelerate research on Machine Learning. Machine learning (ML) has achieved considerable successes in recent years and an ever-growing number of disciplines rely on it. However, this success crucially relies on human machine learning experts to perform manual tasks. As the complexity of these tasks is often beyond non-ML-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML.


Sequential Model-based Algorithm Configuration is a state-of-the-art tool to optimize the performance of your algorithm by determining a well-performing parameter setting.

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Auto-Sklearn is an automated machine learning toolkit to automatically determine a well-performing machine learning pipeline. It is a drop-in replacement for a scikit-learn estimator

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BOHB combines the benefits of both Bayesian Optimization and HyperBand, in order to achieve the best of both worlds: strong anytime performance and fast convergence to optimal configurations.

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