2009, Knowledge Discovery, Machine Learning and Risk

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

Machine Learning algorithms often need to handle highly-imbalanced datasets. This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning, 57–78. 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

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

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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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