{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/17"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/10"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/20"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"Cleanlab automatically detects and helps fix data quality issues\u2014mislabeled examples, outliers, duplicates, and annotation errors\u2014in datasets for machine learning, working with any model and any data modality.","skillfed_tags":["data-quality","label-errors","ml-debugging"],"use_cases":["Identify and correct mislabeled examples in a classification dataset before retraining to improve model accuracy.","Detect duplicate or near-duplicate records in a dataset to reduce redundancy and improve training efficiency.","Find outliers and out-of-distribution examples that may degrade model robustness on production data.","Assess annotation quality in multi-annotator labeling tasks and identify which annotators are unreliable.","Prioritize which unlabeled examples to annotate next to maximize model improvement with minimal labeling effort.","Diagnose data issues in specialized tasks like token classification, image segmentation, or object detection."],"what_it_does":"Cleanlab is a data-centric machine learning library that uses your existing trained model to diagnose and fix dataset problems. Rather than focusing on model architecture improvements, it helps you identify label errors, outliers, duplicates, and other data quality issues by analyzing model predictions and feature embeddings. The core approach, called confident learning, estimates which labels are likely incorrect even when the model itself is imperfect.\n\nYou provide cleanlab with your dataset, model predictions, and optionally feature embeddings. It then reports which examples are problematic and why\u2014whether due to annotation errors, data duplicates, or being out-of-distribution. The library works with any model (PyTorch, TensorFlow, scikit-learn, XGBoost, etc.) and any data type (text, images, audio, tabular). Common workflows involve iterating between cleaning data and retraining to progressively improve model performance without changing your modeling code.","worth_installing":"Yes. Cleanlab is well-maintained, permissively licensed, has low installation friction, and addresses a real problem\u2014most ML projects spend effort optimizing models when data quality is the actual bottleneck. The library is backed by peer-reviewed research, works with any model, and offers concrete value even in a single iteration. Start with it if you suspect label errors or data quality issues are limiting your model's performance."},"id":"cleanlab","links":{"html":"https://skillfed.io/packages/cleanlab","md":"https://skillfed.io/packages/cleanlab.md","pypi":"https://pypi.org/project/cleanlab/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2026-01-13","license_spdx":null,"license_treatment":"permissive","name":"cleanlab","python_support":"supports_current","summary":"The standard package for data-centric AI, machine learning with label errors, and automatically finding and fixing dataset issues in Python."},"popularity":{"monthly_downloads":95735,"position":13251,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.9.0"}