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Natalie Hockham: Machine learning with imbalanced data sets
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- čas přidán 18. 08. 2024
- 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.
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Good presentation. Thanks for posting
Thank you! very intuitive.
NOW we're talking about useful knawlegde!!