2009, Knowledge Discovery, Machine Learning and Testing

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

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. PDPs for the bicycle count prediction model (Molnar, 2009).

Modeling

Modeling Deep Learning Machine Learning Knowledge Discovery

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

Machine Learning algorithms often need to handle highly-imbalanced datasets. Their tests are performed using C4.5-generated note that this variant “performs worse than plain under-sampling based on AUC” when tested on the Adult dataset (Dua & Graff, 2017). Machine Learning, 57–78. Chawla et al.,

Machine Learning

Machine Learning Metrics Data mining Knowledge Discovery

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

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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