For over half a decade, we’ve been increasing the coverage of ML tasks that cleanlab supports, in response to user requests. Today we are proud to share that cleanlab directly supports improving the reliability/accuracy of datasets and models for all major ML tasks. We just released v2.5 of the package, which adds support for regression— yes, cleanlab supports data with real-valued targets now, not just class labels! (nod to the dozens of users who have requested that feature over the years). With v2.5, cleanlab is now the only package to support label error detection for both object detection and image segmentation as well, enabling users to find issues in their datasets for every major ML task across most data types and modalities.
Although it’s brand new, Cleanlab’s regression functionality has already been used to successfully improve data quality in Kaggle competitions. Specifically, a new regression module has been introduced for label error detection in regression datasets like the one below.
Learn how to apply it to your own regression data within 5 minutes via the Find Noisy Labels in Regression Datasets quickstart tutorial. Here’s all the code needed to detect erroneous values in a regression dataset and fit a more robust regression model with the corrupted data automatically filtered out: