Active learning is a special case of machine learning in which a learning algorithm can interactively query a user to label new data points with the desired outputs.
How Active Learning Works
- Start with a small labeled dataset and train an initial model.
- Have a much larger pool of unlabeled data.
- The model makes predictions on the unlabeled data.
- It then identifies instances where it's most uncertain (e.g., those for which the predicted class probabilities are closest to 50% in a binary classification task).
Query for Labels:
- The model (or system) queries the user or expert to label the instances it's uncertain about.
Update the Model:
- Incorporate the newly labeled instances into the training dataset.
- Retrain the model using the updated dataset.
- The process is repeated, with the model iteratively selecting uncertain data points, querying for their labels, and updating its knowledge.
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