Natalie Hockham: Machine learning with imbalanced data sets скачать в хорошем качестве

Natalie Hockham: Machine learning with imbalanced data sets

Classification algorithms tend to perform poorly when data is skewed towards one class, as is often the case when tackling real-world problems such as fraud detection or medical diagnosis. A range of methods exist for addressing this problem, including re-sampling, one-class learning and cost-sensitive learning. This talk looks at these different approaches in the context of fraud detection. Full details — http://london.pydata.org/schedule/pre... 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVi...