2009, Knowledge Discovery, Measurement and Risk

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

This renders measures like classification accuracy meaningless. This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. The use of multiple measurements in taxonomic problems. Chawla et al. Indeed, in the original paper Chawla et al. Dua, D., & Graff, C. Quinlan, J.

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

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. but it generally relies on measuring the entropy in the change of predictions given a perturbation of a feature.

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

Webinars

Stay Connected