How to Perform Effective Data Cleaning for Machine Learning

Author(s): Eivind Kjosbakken

Originally published on Towards AI.

Learn how you can improve your machine learning models using effective data cleaning

Data cleaning is arguably the most important step you can perform in your machine-learning pipeline. Without data, your model algorithm improvements likely won’t matter. After all, the saying ‘garbage in, garbage out’ is not just a saying, but an inherent truth within machine learning. Without proper high-quality data, you will struggle to create a high-quality machine learning model.

The article discusses the critical role of data cleaning in machine learning and outlines three techniques: clustering, Cleanlab, and predicting to compare with annotations. It emphasizes the need for high-quality data over quantity and the importance of maintaining a short experimental loop for efficient model retraining and validation. The techniques aim to identify potentially incorrect labels in datasets, thereby enhancing model performance while reducing the time needed for manual reviews.

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Published via Towards AI