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auto-sklearn是什么?
auto-sklearn是一個自動化機器學習的工具包,其基於sklearn編寫.
>>> import autosklearn.classification
>>> cls = autosklearn.classification.AutoSklearnClassifier()
>>> cls.fit(X_train, y_train)
>>> predictions = cls.predict(X_test)
auto-sklearn可以進行機器學習算法的自動選擇與超參數的自動優化,它使用的技術包括貝葉斯優化,元學習,以及集成機構?(ensemble construction).你可以通過這篇文章,NIPS 2015來學習關於更多auto-sklearn背后的原理與技術.
例子
>>> import autosklearn.classification
>>> import sklearn.model_selection
>>> import sklearn.datasets
>>> import sklearn.metrics
>>> X, y = sklearn.datasets.load_digits(return_X_y=True)
>>> X_train, X_test, y_train, y_test = \
sklearn.model_selection.train_test_split(X, y, random_state=1)
>>> automl = autosklearn.classification.AutoSklearnClassifier()
>>> automl.fit(X_train, y_train)
>>> y_hat = automl.predict(X_test)
>>> print("Accuracy score", sklearn.metrics.accuracy_score(y_test, y_hat))
如果將上面的代碼運行一個小時,那么其精度將會高於0.98.
手冊
手冊中文翻譯
許可證
auto-sklearn與scikit-sklearn的許可證一樣,即都為三條款的BSD許可
援引auto-sklearn
如果你在科學出版物上使用auto-sklearn,我們將感激不盡
Efficient and Robust Automated Machine Learning, Feurer et al., Advances in Neural Information Processing Systems 28 (NIPS 2015).
Bibtex entry:
@incollection{NIPS2015_5872,
title = {Efficient and Robust Automated Machine Learning},
author = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina and
Springenberg, Jost and Blum, Manuel and Hutter, Frank},
booktitle = {Advances in Neural Information Processing Systems 28},
editor = {C. Cortes and N. D. Lawrence and D. D. Lee and M. Sugiyama and R. Garnett},
pages = {2962--2970},
year = {2015},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf}
}
貢獻
我們感謝所有對auto-sklearn做出貢獻的人,無論你是寫的bug報告還是文檔,亦或是新的貢獻.同時如果你想要貢獻代碼.你可以使用issue tracker
同時為了項目合並前避免重復的工作,強烈建議你在進行工作前與我們的工作人員在(github issues)[https://github.com/automl/auto-sklearn/issues]上進行聯系
同時建議你在開發新的功能時,請先創建新的發展分支,同時在所有的測試結束並通過后,進行項目合並.