How to Handle an Imbalanced Dataset In Machine Learning Using SMOTE

Author(s): Tanesh balodi

Originally published on Towards AI.

How to Handle an Imbalanced Dataset In Machine Learning Using SMOTE

All that people ask for in a machine learning model is the accuracy of the model; this accuracy is sometimes nothing but a hoax. There are a lot of factors that determine the accuracy of the model, the major one among them is the quality of the dataset. The preparation of data is the most fundamental step in machine learning models.

The article discusses the challenges posed by imbalanced datasets in machine learning, explaining how such datasets can lead to misleading accuracy metrics. It highlights various methods to address these issues, such as undersampling, oversampling, and SMOTE (Synthetic Minority Over-sampling Technique). Each method is detailed with examples, demonstrating their applications and the potential pitfalls associated with each approach.

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Published via Towards AI