# Cleanlab — Find and Fix Label Errors in Any ML Dataset

> Cleanlab is a data-centric AI Python library that automatically detects label errors, outliers, and data quality issues in classification and regression datasets, helping improve model accuracy by cleaning training data rather than tuning models.

## Install

Save in your project root:

## Quick Use
```bash
pip install cleanlab
python -c "from cleanlab import Datalab; print('Cleanlab ready')"
```

## Introduction
Cleanlab is an open-source Python library for data-centric AI that finds and fixes problems in ML datasets. Rather than improving models through architecture changes or hyperparameter tuning, Cleanlab improves the data itself by identifying mislabeled examples, near-duplicates, outliers, and other quality issues using confident learning algorithms.

## What Cleanlab Does
- Detects mislabeled examples in classification, multi-label, and regression datasets
- Identifies near-duplicate, outlier, and ambiguous data points
- Ranks every example by a data quality score for prioritized human review
- Works with any trained classifier via out-of-sample predicted probabilities
- Integrates with scikit-learn, PyTorch, TensorFlow, and Hugging Face Transformers

## Architecture Overview
Cleanlab implements confident learning, a framework that estimates the joint distribution of noisy observed labels and true latent labels using out-of-sample predicted probabilities from any classifier. The Datalab class orchestrates multiple issue checks (label errors, outliers, duplicates) in a single audit pass. It computes per-sample quality scores without retraining the model, making it model-agnostic and efficient.

## Self-Hosting & Configuration
- Install via `pip install cleanlab` with optional extras for specific frameworks
- Pass a trained model or pre-computed predicted probabilities to `Datalab.find_issues()`
- Use cross-validation helpers to generate out-of-sample predictions automatically
- Works with NumPy arrays, pandas DataFrames, and Hugging Face Dataset objects
- No GPU required; all analysis runs on CPU

## Key Features
- Model-agnostic: works with any classifier that outputs predicted probabilities
- Handles multi-class, multi-label, token classification, and regression tasks
- Provides interpretable per-sample quality scores and issue explanations
- Scales to millions of examples on commodity hardware
- Backed by peer-reviewed research on confident learning

## Comparison with Similar Tools
- **Argilla** — data labeling and curation platform; broader scope but less automated error detection
- **Label Studio** — annotation tool; handles labeling workflow but does not detect errors automatically
- **Great Expectations** — data validation for pipelines; checks schema and distribution, not label correctness
- **Evidently** — ML monitoring and data drift detection; focused on production rather than training data
- **DataProfiler** — statistical profiling; finds schema issues, not label errors

## FAQ
**Q: Do I need to retrain my model to use Cleanlab?**
A: No. Cleanlab works with predicted probabilities from any already-trained model. Cross-validation helpers can generate these for you.

**Q: How accurate is the label error detection?**
A: Confident learning has been shown to identify real label errors with high precision across benchmarks. Results depend on the quality of the underlying classifier.

**Q: Can I use it for text and image datasets?**
A: Yes. Cleanlab is data-type agnostic. As long as you can train a classifier and get predicted probabilities, it works.

**Q: Does it modify my dataset automatically?**
A: No. Cleanlab identifies issues and provides quality scores. You decide whether to relabel, remove, or keep flagged examples.

## Sources
- https://github.com/cleanlab/cleanlab
- https://docs.cleanlab.ai/

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Source: https://tokrepo.com/en/workflows/02c6e577-42ba-11f1-9bc6-00163e2b0d79
Author: AI Open Source