Data Collection, Knowledge Discovery, Measurement and Testing

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

Working with highly imbalanced data can be problematic in several aspects: Distorted performance metrics — In a highly imbalanced dataset, say a binary dataset with a class ratio of 98:2, an algorithm that always predicts the majority class and completely ignores the minority class will still be 98% correct. 1988), E-state data (Hall 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

After forming the X and y variables, we split the data into training and test sets. Looking at the target vector in the training subset, we notice that our training data is highly imbalanced. but it generally relies on measuring the entropy in the change of predictions given a perturbation of a feature. See Wei et al.

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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