Clean ML Datasets With Cleanlab

...in just 4 lines of Python code.

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Clean ML Datasets With Cleanlab

For the longest time, no one could get past the 91% accuracy on ImageNet (92.4% is quite recent).

ImageNet leaderboard on Paperwithcode

Why?

It was found that ImageNet had over 100k mislabeled images.

Real-world datasets are messy.

They often come with noisy labels, missing values, and outliers that can severely degrade your model’s performance.

No sophisticated ML algorithms can compensate for poor-quality or mislabeled data.

Researchers from MIT developed Cleanlab, which is an open-source library that cleans your data in just a few lines of code.

As shown in the image above, Cleanlab can flag errors in any type of data (text, image, tabular, audio), like:

• out-of-distribution samples

• outliers

• label issues

• duplicates, etc.

All it takes is just four lines of code:

• Import the package.

• Pass the dataset and specify the label column.

• Find issues by passing the embedding matrix and the probabilities predicted by the model.

• Finally, generate the report!

Done!

It will generate a report like the one shown above.

This way, you can easily clean your datasets for training accurate ML models.

Isn’t that impressive?

Several notebook demos are available here if you want to learn more: Cleanlab demo.

Cleanlab GitHub repo: GitHub repository

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P.S. For those wanting to develop “Industry ML” expertise:

At the end of the day, all businesses care about impact. That’s it!

• Can you reduce costs?

• Drive revenue?

• Can you scale ML models?

• Predict trends before they happen?

We have discussed several other topics (with implementations) in the past that align with such topics.

Here are some of them:

• Learn techniques to run large models on small devices: Quantization: Optimize ML Models to Run Them on Tiny Hardware

• Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust: Conformal Predictions: Build Confidence in Your ML Model’s Predictions.

• Learn how to identify causal relationships and answer business questions: A Crash Course on Causality – Part 1

• Learn how to scale ML model training: A Practical Guide to Scaling ML Model Training.

• Learn techniques to reliably roll out new models in production: 5 Must-Know Ways to Test ML Models in Production (Implementation Included)

• Learn how to build privacy-first ML systems: Federated Learning: A Critical Step Towards Privacy-Preserving Machine Learning.

• Learn how to compress ML models and reduce costs: Model Compression: A Critical Step Towards Efficient Machine Learning.

All these resources will help you cultivate key skills that businesses and companies care about the most.

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