What it is: ML-powered data cleaning library. Automatically finds label errors, outliers, and near-duplicates in your training data using machine learning confidence scores.

What It Does Best

Finding mislabeled data. Uses cross-validation and model uncertainty to identify training examples with wrong labels. Works with any ML framework.

Data-centric AI. Improve model performance by fixing data rather than tweaking hyperparameters. Often gives bigger gains than model optimization.

Works with existing models. Integrates with scikit-learn, PyTorch, TensorFlow, Hugging Face. No need to change your workflow.

Key Features

Label error detection: Automatically finds mislabeled training examples

Outlier detection: Identifies unusual or anomalous data points

Near-duplicate detection: Finds similar examples in datasets

Framework agnostic: Works with scikit-learn, PyTorch, TensorFlow, XGBoost

Confidence scoring: Ranks issues by severity for efficient review

Pricing

Open source: Free, AGPL license (Python library)

Cleanlab Studio: Custom pricing (hosted platform with GUI)

Enterprise: Commercial licensing available for proprietary use

When to Use It

✅ Training ML models with human-labeled data

✅ Model underperforming and you suspect bad labels

✅ Working with crowdsourced or noisy datasets

✅ Computer vision or NLP classification tasks

✅ Need to audit dataset quality before production

When NOT to Use It

❌ No labeled data (unsupervised learning)

❌ Very small datasets (need enough data for cross-validation)

❌ Time-series or regression problems (optimized for classification)

❌ Perfectly clean synthetic data

❌ Simple rule-based data validation is sufficient

Common Use Cases

Image classification: Find mislabeled images in training datasets

NLP tasks: Identify wrong labels in text classification

Medical imaging: Audit labels from multiple radiologists

Crowdsourced data: Clean labels from Amazon Mechanical Turk

Active learning: Prioritize which examples to re-label

Cleanlab vs Alternatives

vs Manual inspection: Cleanlab 100x faster, finds issues humans miss

vs Great Expectations: Cleanlab for ML labels, GE for data pipelines

vs Snorkel: Cleanlab finds errors, Snorkel generates weak labels

Unique Strengths

Confident learning: Novel algorithm for finding label errors

Model-agnostic: Use any classifier, even ensemble methods

Research-backed: Published in top ML conferences (NeurIPS, ICML)

Production-ready: Used by Google, Amazon, Meta for data quality

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