cleanlab/cleanlab

Features

• Confident Learning Label Cleaning - Locates mislabeled examples using confident learning algorithms to improve overall data quality.
• Class-Conditional Noise Modeling - Models the probability of labels being flipped between classes to quantify dataset-wide noise levels.
• Dataset Health Analysis - Calculates class-level issues and generates quality summaries to determine if a dataset is reliable for training.
• Dataset Label Error Detection - Finds and corrects mislabeled examples in training data using confident learning to improve model accuracy.
• Dataset Quality Analysis - Measures the reliability of labels and identifies class-level issues to determine if a dataset is fit for training.
• Dataset Quality Analyzers - Provides utilities to quantify annotator reliability and identify class-level label issues to assess dataset health.
• Confident Learning Frameworks - Iteratively refines label noise estimates by comparing model predictions with estimated label probabilities to identify mislabeled examples.
• Data Cleaning Procedures - Identifies outliers, duplicates, and atypical data points to refine and clean machine learning training sets.
• Anomaly Detection - Identifies atypical data points and duplicates that fall outside expected distributions to refine training sets.
• Cross-Validation Probability Estimation - Generates out-of-sample predicted probabilities for training data to avoid overfitting during label error detection.
• Noise-Robust Training - Enables the creation of models that remain accurate even when training data contains noisy or incorrect labels.
• Active Learning - Selects the most impactful examples for labeling or re-labeling to increase machine learning model performance.
• Sample Selection Logic - Implements logic to prioritize high-uncertainty or high-impact samples for manual review to maximize model improvement.
• Label Optimization - Selects the most impactful data points for human re-labeling to maximize the performance gain of a model.
• Out of Distribution Detection - Identifies anomalous data points by analyzing the distance between individual samples and their class centroids.
• Anomaly Detection - Finds atypical data points that fall outside the expected distribution to remove or investigate anomalies.
• Annotation Quality Verifications - Quantifies individual annotator reliability by calculating consensus across datasets labeled by multiple people.
• Consensus-Based Label Verification - Calculates annotator reliability by measuring agreement across multiple labels for the same data point.
• Data Centric AI - Identifies and cleans noisy labels in real-world datasets.
• General Machine Learning - Package for data quality and learning with noisy labels.
• Machine Learning Libraries - Data-centric AI for identifying label errors.
• Data Annotation and Synthesis - Library for detecting mislabeled data and data-centric AI.
• Data Processing and Analysis - Data-centric AI for detecting dataset issues.
• Data Validation - Library for identifying issues in real-world datasets.
• Data Validation and Cleaning - Detecting and fixing noisy labels in datasets.
• Data Science Tooling - Automated detection of issues in ML datasets.
• Data Science Tools - Automated detection of issues in ML datasets.

Star history