Active Learning

Implement active learning strategies (uncertainty sampling, query-by-committee, expected model change) to intelligently select data for annotation, significantly reducing labeling costs while maximizing model performance.

intermediate3-4 hours4 steps

The play

Set up the Environment
Install necessary libraries for active learning and a machine learning framework (e.g., scikit-learn).
Implement Uncertainty Sampling
Write a function to calculate the uncertainty of model predictions on unlabeled data. Use the least confidence or margin sampling approach.
Implement Query-by-Committee
Train multiple models on the labeled data and select the unlabeled instances where the models disagree the most.
Simulate Active Learning Loop
Create a loop that iteratively trains a model, selects data points for labeling using an active learning strategy, and updates the labeled dataset.

Starter code

# Implement active learning strategies to intelligently select data for annotation.

Source

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