2009, Knowledge Discovery, Risk and Visualization

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

Figure 3 shows visual explanation of how SMOTE generates synthetic observations in this case. This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79. Chawla et al.

Machine Learning

Machine Learning Metrics Data mining Knowledge Discovery

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

Domino Data Lab

AUGUST 1, 2021

Skater provides a wide range of algorithms that can be used for visual interpretation (e.g. This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. This is to be expected, as there is no reason for a perfect 50:50 separation of the good vs. bad credit risk.

Modeling

Modeling Deep Learning Machine Learning Knowledge Discovery

Data Leaders Brief

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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

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