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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. return synthetic.

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Mobile Marketing 2015: Rethink Customer Acquisition, Intent Targeting

Occam's Razor

2009 was the year of mobile. If your company has a non-stinky mobile website and mobile app then congratulations: you have successfully solved the problem of 2009! I'm sure you are impressed at the data mining and intent targeting efforts of TripIt. Measurement? Everything described above is measureable.

Marketing 144
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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

Because of its architecture, intrinsically explainable ANNs can be optimised not just on its prediction performance, but also on its explainability metric. but it generally relies on measuring the entropy in the change of predictions given a perturbation of a feature. PDPs for the bicycle count prediction model (Molnar, 2009).

Modeling 139