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.
4 Steps
- 1
Set up the Environment: Install necessary libraries for active learning and a machine learning framework (e.g., scikit-learn).
- 2
Implement Uncertainty Sampling: Write a function to calculate the uncertainty of model predictions on unlabeled data. Use the least confidence or margin sampling approach.
- 3
Implement Query-by-Committee: Train multiple models on the labeled data and select the unlabeled instances where the models disagree the most.
- 4
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.
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