2017, Data Collection, Knowledge Discovery and Risk

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

Domino Data Lab

AUGUST 1, 2021

For this demo we’ll use the freely available Statlog (German Credit Data) Data Set, which can be downloaded from Kaggle. This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. Conference on Knowledge Discovery and Data Mining, pp. References.

Modeling

Modeling Deep Learning Machine Learning Knowledge Discovery

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large. This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. Chawla et al. link] Ling, C.

Machine Learning

Machine Learning Metrics Data mining Knowledge Discovery

Data Leaders Brief

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

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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