之前在IOP的那篇论文里提到过,反演问题的数学本质是一个病态问题。而机器学习和深度学习本质上解决的都是分类和回归问题,并不能直接解决病态问题。最近重新查看一些论文,终于想起了之前看过的一些东西。

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因为研究的原因又回到了老本行,要用auto-sklearn和XGBoost,这次一定要把我自己讲明白

一些地址

https://towardsdatascience.com/beyond-grid-search-hypercharge-hyperparameter-tuning-for-xgboost-7c78f7a2929d

https://issueexplorer.com/issue/automl/auto-sklearn/1297

https://www.kaggle.com/residentmario/automated-feature-selection-with-sklearn

https://stackoverflow.com/questions/54035645/features-and-feature-importance-in-auto-sklearn-with-one-hot-encoded-features

https://github.com/automl/auto-sklearn/issues/524

https://towardsdatascience.com/feature-preprocessor-in-automated-machine-learning-c3af6f22f015

https://scikit-learn.org/stable/modules/feature_selection.html

https://github.com/automl/auto-sklearn/issues/524

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之前就想了各种各样的方法来跳过GDAL,没想到最后工作了还是要用这个

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之前一直想方设法跨过去的事情今天终于要开始搞了

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