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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Their tests are performed using C4.5-generated note that this variant “performs worse than plain under-sampling based on AUC” when tested on the Adult dataset (Dua & Graff, 2017). Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79. Chawla et al., 1998) and others).

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

Domino Data Lab

After forming the X and y variables, we split the data into training and test sets. Next, we pick a sample that we want to get an explanation for, say the first sample from our test dataset (sample id 0). For sample 23 from the test set, the model is leaning towards a bad credit prediction. show_in_notebook(). References.

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