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What is SMOTE (Synthetic Minority Over-Sampling Technique)?

SMOTE is a data science tool that mitigates the challenges of imbalances in data sets by synthesizing instances of the under-represented data.

Overview

What it is:

SMOTE is a data science tool that mitigates the challenges of imbalances in datasets for machine learning, such as accuracy rate.

What it does:

SMOTE synthesizes instances of the under-represented data set, to reduce potential for imbalances.

Why it matters:

Imbalances in training data can make artificial intelligence unreliable, increasing risk and undermining trust.

What to do about it:

Data scientists can use SMOTE tools and techniques when they are concerned about potential weaknesses in data sets. As an ML decision maker, you can ensure that your data scientists have all the necessary tools to create reliable models, with SMOTE as an element of the toolset.

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