Introduction

Training machine learning models on noisy, real-world data often leads to suboptimal performance and unpredictable errors. Cleanlab is a data-centric AI library that automates the detection of label errors, outliers, and other data issues to ensure your models are trained on the highest quality data. With over 11.5k GitHub stars, it has become the industry standard for practitioners who want to improve model accuracy without changing their modeling code.

What Is Cleanlab?

Cleanlab is an open-source Python library that automatically detects issues in machine learning datasets by leveraging the predicted class probabilities of existing models. It is designed for data scientists and ML engineers who need to clean messy, real-world data and labels to improve model reliability.

Maintained by a team of experts from MIT, Cleanlab is licensed under the Apache License 2.0. It works across all modalities—including text, image, audio, and tabular data—and is compatible with any ML framework, such as PyTorch, TensorFlow, Keras, JAX, HuggingFace, XGBoost, and scikit-learn.

Why Cleanlab Matters

In traditional ML workflows, the focus is almost always on the model architecture and hyperparameters. However, the “data-centric AI” philosophy argues that the data itself is the primary lever for performance. Cleanlab fills this gap by providing a systematic way to find and fix label noise, which is a common but often ignored problem in large-scale datasets.

The impact of label noise can be devastating: a model trained on incorrectly labeled data will learn the noise rather than the signal. By automatically identifying the most likely label errors, Cleanlab allows teams to focus their manual labeling efforts on the examples that actually matter, drastically reducing the cost and time required for data curation.

With significant traction in the community and proven results on benchmarking datasets like ImageNet and MNIST, Cleanlab provides a mathematically rigorous approach to data cleaning that replaces guesswork with automated, confidence-based auditing.

Key Features

• Automated Label Error Detection: Uses confident learning algorithms to detect label issues in any classification dataset, identifying examples where the model’s prediction strongly contradicts the given label.
• Outlier and Duplicate Detection: Identifies data points that are far from the rest of the dataset or near-duplicates that can lead to overfitting or data leakage between training and test sets.
• Multi-Annotator Support: Infers consensus labels and estimates the quality of individual annotators when dealing with data labeled by multiple imperfect humans.
• Active Learning Suggestions: Suggests which data points should be re-labeled next to maximize the improvement in model performance.
• Framework Agnostic: Operates on model outputs (predicted probabilities and feature embeddings) rather than the model itself, making it compatible with any framework (PyTorch, TensorFlow, etc.).
• Cross-Modality Compatibility: Works seamlessly with text, audio, image, or tabular datasets, as long as a classifier can be trained on them.
• Datalab Interface: Provides a high-level Datalab class that simplifies the process of finding issues and generating reports on dataset health.

How Cleanlab Compares

Cleanlab differs from traditional data cleaning tools (like pandas or dbt) which focus on structural cleaning (missing values, formatting) rather than semantic cleaning (label errors). While some libraries like MentorNet focus on specific noise-robust training, Cleanlab focuses on the dataset itself.

The primary differentiator for Cleanlab is its ability to use the model’s own uncertainty to find errors. Unlike rule-based cleaning, it doesn’t require the user to define what a “bad” example looks like; it discovers the errors based on the model’s predicted probabilities. This makes it far more scalable than manual auditing and more flexible than framework-specific noise-robust training.

Getting Started: Installation

Cleanlab is available via several package managers. It is recommended to use a Python 3.10+ environment.

Installation via pip

pip install cleanlab

To install the package with all optional dependencies for full functionality:

pip install "cleanlab[all]"

Installation via conda

conda install -c cleanlab cleanlab

Installation from source

pip install git+https://github.com/cleanlab/cleanlab.git

How to Use Cleanlab

The most straightforward way to use Cleanlab is through the Datalab class. The general workflow involves training a model, getting its predicted probabilities for the training set, and then using Cleanlab to find issues.

First, train your classifier of choice (e.g., using scikit-learn or PyTorch) and use it to generate pred_probs (predicted class probabilities) for your dataset. These probabilities are the key input for Cleanlab’s algorithms.

Once you have the probabilities, you initialize a Datalab instance with your dataset and the name of the label column. You then call find_issues(), which analyzes the probabilities against the labels to identify label errors, outliers, and duplicates.

Code Examples

Below is a basic example of how to use Cleanlab to detect label issues in a classification dataset.

from cleanlab import Datalab
import pandas as pd

# Load your dataset
data = pd.read_csv("your_dataset.csv")
# Assume you have a column 'labels' and predicted probabilities from your model
# pred_probs should be a numpy array of shape (N, K)
pred_probs = model.predict_proba(data)

# Initialize Datalab
lab = Datalab(data=data, label="labels")

# Find issues using predicted probabilities and feature embeddings
lab.find_issues(features=None, pred_probs=pred_probs)

# Generate a report on the data issues found
lab.report()

This snippet demonstrates the core Datalab workflow: loading data, providing model outputs, and printing a summary of the report. Cleanlab can also be used for more complex tasks like iterative cleaning of a dataset to boost model performance.

Real-World Use Cases

Cleanlab is particularly effective in domains where human labeling is expensive or expensive errors are costly.

• Medical Imaging: A radiologist’s labels may contain errors. Cleanlab can identify the most likely mislabeled images, allowing the expert to review only a small subset of the case files to correct them.
• Fraud Detection: In tabular datasets, fraud labels are often noisy. Cleanlab can detect outliers and label errors that help refine the fraud detection model’s decision boundary.
• 
  Customer Support AI: When fine-tuning an LLM for classification, Cleanlab can be used to identify bad instruction-tuning data that might lead to the model hallucinating or providing incorrect responses.

Contributing to Cleanlab

Cleanlab is an open-source project that encourages contributions from the community. If you are interested in contributing, you should first review the CONTRIBUTING.md file in the repository. The maintainers suggest starting with issues tagged as “good first issue” to get acclimated to the code base.

The project follows a standard GitHub flow: report bugs via the issue tracker, submit feature requests as enhancements, and provide a detailed description of your context (Python version, Cleanlab version) when reporting bugs. All contributions must be signed by the Contributor License Agreement (CLA) and pass the test suite.

Community and Support

Cleanlab provides several official channels for support and support. The primary hub for community discussion is the Slack Community, where scientists and engineers can discuss Data-Centric AI practices. There is also a comprehensive documentation site and a detailed set of tutorials and notebooks demonstrating real-world applications.

The project is also active on GitHub Discussions, providing a long-term record of of the community’s questions and answers. The community size is notable, with thousands of stars and hundreds of forks, indicating a high level of activity and maintenance.

Conclusion

Cleanlab is the essential tool for any machine learning practitioner who has moved beyond the model-centric approach and is now focusing on the data-centric AI paradigm. By automating the label cleaning process, it allows teams to build more reliable models on real-world, noisy data without the need for manual, exhaustive auditing.

It is the right choice when you have a large dataset and suspect label noise, or when you have data labeled by multiple imperfect annotators. It is not a replacement for a complete data pipeline, but rather a specialized layer that sits between your model and your dataset to ensure quality.

Star the repo, try the quickstart, and join the community to start improving your model performance by cleaning your data.

Resources

• Cleanlab GitHub Repository
• Official Cleanlab Documentation
• Cleanlab AI Platform