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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

The LSOS may do this by exposing a random group of users to the new design and compare them to a control group, and then analyze the effect on important user engagement metrics, such as bounce rate, time to first action, or number of experiences deemed positive. In addition to a suitable metric, we must also choose our experimental unit.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. def get_neigbours(M, k): nn = NearestNeighbors(n_neighbors=k+1, metric="euclidean").fit(M) Data mining for direct marketing: Problems and solutions. return synthetic. Chawla et al.

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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. For this demo we’ll use the freely available Statlog (German Credit Data) Data Set, which can be downloaded from Kaggle. 1 570 0 570 Name: credit, dtype: int64.

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