2009, Knowledge Discovery and Testing

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

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). Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79. Chawla et al., 1998) and others).

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

After forming the X and y variables, we split the data into training and test sets. PDPs for the bicycle count prediction model (Molnar, 2009). Next, we pick a sample that we want to get an explanation for, say the first sample from our test dataset (sample id 0). Conference on Knowledge Discovery and Data Mining, pp.

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

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