Last updated: November 13, 2025 by the editorial team
Author's): Tanesh the pigeon
Originally published in Towards Artificial Intelligence.
How to deal with an imbalanced dataset in machine learning with SMOTE
The only thing people ask for in a machine learning model is the accuracy of the model; this accuracy is sometimes nothing more than fraud. Many factors influence the accuracy of the model, the most important of which is the quality of the data set. Data preparation is the most basic step in machine learning models.
This article discusses the challenges posed by unbalanced datasets in machine learning, explaining how such datasets can lead to misleading accuracy rates. It highlights various methods to solve these problems, such as subsampling, oversampling, and SMOTE (Synthetic Minority Oversampling Technique). Each method is described in detail with examples demonstrating its applications and the potential pitfalls of each approach.
Read the entire blog for free on Medium.
Published via Towards AI
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