Auto-sklearn provides out-of-the-box supervised machine learning. Built around the scikit-learn machine learning library, auto-sklearn automatically searches for the right learning algorithm for a new machine learning dataset and optimizes its hyperparameters. Thus, it frees the machine learning practitioner from these tedious tasks and allows her to focus on the real problem.
auto-sklearn is written in python and is a drop-in replacement for scikit-learn classifiers:
>>> import autosklearn.classification >>> cls = autosklearn.classification.AutoSklearnClassifier() >>> cls.fit(X_train, y_train) >>> predictions = cls.predict(X_test, y_test)
Auto-sklearn extends the idea of configuring a general machine learning framework with efficient global optimization which was introduced with Auto-WEKA. To improve generalization, auto-sklearn builds an ensemble of all models tested during the global optimization process. In order to speed up the optimization process, auto-sklearn uses meta-learning to identify similar datasets and use knowledge gathered in the past. Auto-sklearn wraps a total of 15 classification algorithms, 14 feature preprocessing algorithms and takes care about data scaling, encoding of categorical parameters and missing values.
We developed auto-sklearn to participate in the ChaLearn Automatic Machine Learning Challenge. Using auto-sklearn, we won six out of 10 track. You can read an overview paper by the competition organizers here.
Feurer, M. and Klein, A. and Eggensperger, K. and Springenberg, J. and Blum, M. and Hutter, F.
Efficient and Robust Automated Machine Learning
In: Advances in Neural Information Processing Systems 28