2009, Knowledge Discovery, Measurement and Visualization

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

This renders measures like classification accuracy meaningless. Figure 3 shows visual explanation of how SMOTE generates synthetic observations in this case. The use of multiple measurements in taxonomic problems. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79.

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. 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). Conference on Knowledge Discovery and Data Mining, pp.

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Modeling Deep Learning Machine Learning Knowledge Discovery

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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

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