2009, Knowledge Discovery 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. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79. UCI machine learning repository. link] Fisher, R. The use of multiple measurements in taxonomic problems. link] Halevy, A. link] Hall, L.,

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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. Partial Dependence Plot is another visual method, which is model agnostic and can be successfully used to gain insights into the inner workings of a black-box model like a deep ANN. PDPs for the bicycle count prediction model (Molnar, 2009).

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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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