Researchers Release Cleanlab 2.0: An Open-Source Python Framework For Machine Learning And Analytics With Messy, Real-World Data

Data preparation is the most time-consuming and hectic process in data science and machine learning, accounting for 80% of the labor. Messy data is a serious issue that costs businesses trillions of dollars every year. Model performance can be harmed by data errors (for example, mislabeled samples in the training set) and dataset-level concerns like overlapping classes. Most test set errors are ubiquitous even in gold-standard benchmark datasets. This can cause data scientists to deploy worse models. Although physically analyzing and cleaning up individual data points sounds tiresome, it frequently gives a significantly bigger payback than experimenting with advanced modeling approaches.