description Auto-sklearn Overview
Auto-sklearn is an open-source AutoML tool built on top of scikit-learn. It automatically searches for the best machine learning model for your data, using a gradient-boosting approach. Auto-sklearn is a great option for users familiar with scikit-learn who want to automate the model building process.
help Auto-sklearn FAQ
What does auto-sklearn automate?
Auto-sklearn automates model selection, hyperparameter optimization, and ensemble construction for scikit-learn-style machine-learning tasks. Its documentation describes the system as using Bayesian optimization, meta-learning, and ensemble construction.
Is auto-sklearn just a gradient-boosting model?
No, it searches across a portfolio of scikit-learn algorithms and can build an ensemble of several models. Gradient boosting may be selected during the search, but auto-sklearn itself is an AutoML framework rather than one fixed estimator.
Can auto-sklearn replace a scikit-learn estimator?
It is designed as a drop-in replacement for a scikit-learn estimator in common supervised-learning workflows. You still need to prepare the data, choose the task type, and set a reasonable time and memory budget.
Does auto-sklearn work with classification and regression?
Yes, the project provides separate interfaces for classification and regression tasks. Its automatic search can then evaluate compatible scikit-learn pipelines and combine strong candidates into an ensemble.
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