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Successes, Failures and Learning From Them

Successes, Failures and Learning From Them

This video was recorded at 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Jose 2007. Another topic of interest here is to highlight some of the classic mistakes made in the field. Topics of interest here could range from the use of non-representative training data to the ignorance of population drift when modeling time-varying data, from not accounting for errors in data or labels in the model to an over reliance on a single technique for the task on hand and from asking the wrong question in the context of the application driver to sampling without care. A related topic here might be to think about the role of benchmark datasets and algorithms, and reflect on the general importance and requirement for repeatable and reproducible results.


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