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On the Hunt for Patterns: from Hippocrates to Supercomputers

Ontotext

These are the so-called supercomputers, led by a smart legion of researchers and practitioners in the fields of data-driven knowledge discovery. As of 2017, the fastest computers have reached a speed of 93 PetaFLOPS, which is: 93×1015, or 93,000,000,000,000,000 operations per second. Certainly not!

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

note that this variant “performs worse than plain under-sampling based on AUC” when tested on the Adult dataset (Dua & Graff, 2017). In this blog post we talked about why working with imbalanced datasets is typically problematic, and covered the internals of SMOTE – a go-to technique for up-sampling minority classes.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

Although this blog post makes some specific points about changing assignment weights in an A/B experiment, there is a more general takeaway as well. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2017. [4] Henne, and Dan Sommerfield. 2] Scott, Steven L.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

In IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI), pages 24–30, Melbourne, Australia, 2017. Conference on Knowledge Discovery and Data Mining, pp. The post Explaining black-box models using attribute importance, PDPs, and LIME appeared first on Data Science Blog by Domino. References.

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