Handling Mislabeled Tabular Data to Improve Your XGBoost Model

This article discusses the use of cleanlab to improve the accuracy of an XGBoost classifier by reducing prediction errors on a noisy dataset. The techniques focus on optimizing the dataset itself rather than altering the model's architecture or hyperparameters, allowing for further improvements in accuracy through fine-tuning the model with the enhanced data. Cleanlabel is a powerful tool that can automatically detect and help prioritize potential issues within various types of data, including tabular, image, text, and audio formats. By ensuring the integrity of your data using cleanlab, you can mitigate costly labeling errors and boost the performance of your models.