Valeryi M. Bezruk, Stanislav A. Krivenko, Oleksandr O. Kyrsanov, Sergii S. Kryvenko, L. Kryvenko
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Abstract
Data exploration, wrangling, and interactive analysis and visualization were made in an integrated way. How to plot feature importance in Python calculated by the XGBoost model was considered. Features engineering in a dataset has been improved with Haar Transform. The area under the receiver operating characteristic curve was increased from 0.44 for the baseline model to 0.82 for Haar Transform Model.
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