What is Active Learning? 

Active Learning is a machine learning technique that enables models to effectively learn from limited labelled data by selectively querying the most informative data points for annotation. This approach optimizes the learning process, reducing the overall labelling effort required.

How Active Learning Works 

Active Learning optimizes the model training process by selecting the most informative data points for annotation. Instead of randomly labelling data, it identifies instances where the model is uncertain and requests human input, thereby improving model accuracy with minimal labelled data.

Active Learning Benefits

• Efficiency: Reduces annotation effort by focusing on crucial data points, accelerating model convergence.
• Enhanced Model Performance: Enables models to achieve higher accuracy with fewer labelled examples.
• Cost-Effectiveness: Minimizes the resources needed for data annotation, making machine learning feasible in resource-constrained environments.

Use Cases for Active Learning

• Medical Imaging Analysis: Prioritize critical cases for annotation to develop AI models for disease diagnosis.
• Document Classification: Efficiently categorize documents with uncertain classifications to improve accuracy
• Sentiment Analysis in Customer Support: Enhance sentiment analysis models by focusing on ambiguous feedback.