Cleanlab python library.

Cleanlab is a Python library specifically designed for handling noisy labels in machine learning datasets. It provides tools and methods to identify, visualize, and correct mislabeled instances, ultimately improving the performance and robustness of machine learning models. Let’s dive into some key aspects of Cleanlab:

1. Confident Learning:

• One of the core concepts behind Cleanlab is confident learning. This involves identifying and leveraging instances in the dataset for which the model’s predictions are confident. These instances are considered as potential candidates for being correctly labeled.

2. Noise-Aware Classification:

• Cleanlab introduces noise-aware classifiers, which take into account the possibility of label noise in the training data. These classifiers provide better estimates of the true underlying data distribution.

3. Identification of Noisy Labels:

• Cleanlab offers functions to identify instances in the dataset that are likely to be mislabeled. This is done by analyzing the disagreement between the predicted class probabilities and the assigned labels.

4. Error-Correction:

• Once noisy labels are identified, Cleanlab provides tools for correcting them. This can involve removing instances with high-confidence mislabeled annotations or reassigning labels based on the model’s confidence.

5. Compatibility with Scikit-Learn:

• Cleanlab is designed to be compatible with popular machine learning libraries like Scikit-Learn. This allows users to seamlessly integrate Cleanlab into their existing machine learning workflows.

6. Pruning Methods:

• Cleanlab includes pruning methods, such as get_noise_indices, which can be used to identify indices of likely mislabeled instances. This is particularly useful for creating cleaner datasets for model training.

7. Visualization Tools:

• Cleanlab provides tools for visualizing the relationships between predicted probabilities, true labels, and noisy labels. Visualization can be crucial for gaining insights into the characteristics of label noise in the dataset.

Examples and Documentation:

• Cleanlab comes with examples and documentation that guide users through the process of using its functionalities. This includes step-by-step explanations and sample code to help users apply confident learning techniques to their specific datasets.

Here’s a simplified example:

from cleanlab.pruning import get_noise_indices

# Assuming s is the set of labels, psx is the predicted probabilities, and clf is the classifier
noise_indices = get_noise_indices(s, psx, clf)

In this example, s represents the true labels, psx is the predicted probabilities, and clf is the classifier used to generate predictions. The get_noise_indices function identifies indices of likely mislabeled instances.

Documentation: cleanlab