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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Working with highly imbalanced data can be problematic in several aspects: Distorted performance metrics — In a highly imbalanced dataset, say a binary dataset with a class ratio of 98:2, an algorithm that always predicts the majority class and completely ignores the minority class will still be 98% correct. In their 2002 paper Chawla et al.

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PODCAST: COVID19 | Redefining Digital Enterprises – Episode 13: Digital Sales Enablement is a gamechanger in the post-COVID era

bridgei2i

My name is Aruna Babu, and I’m a transformation consultant who spent a good part of the last decade crafting strategy that marries business technology and user needs. And it was funny cause I was going through a book that my business partner Barry Trailer and I wrote back in 2002. We’ve got, I actually have metrics.

Sales 93
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PODCAST: COVID19 | Redefining Digital Enterprises – Episode 13: Digital Sales Enablement a gamechanger in the post-COVID era

bridgei2i

My name is Aruna Babu, and I’m a transformation consultant who spent a good part of the last decade crafting strategy that marries business, technology and user needs. And it was funny cause I was going through a book that my business partner Barry Trailer and I wrote back in 2002. We’ve got, I actually have metrics.

Sales 52
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Unintentional data

The Unofficial Google Data Science Blog

With more features come more potential post hoc hypotheses about what is driving metrics of interest, and more opportunity for exploratory analysis. Looking at metrics of interest computed over subpopulations of large data sets, then trying to make sense of those differences, is an often recommended practice (even on this very blog).