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Active Learning

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


  1. Start with a small labeled dataset and train an initial model.
  2. Have a much larger pool of unlabeled data.

Uncertainty Sampling:

  1. The model makes predictions on the unlabeled data.
  2. 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:

  1. The model (or system) queries the user or expert to label the instances it's uncertain about.

Update the Model:

  1. Incorporate the newly labeled instances into the training dataset.
  2. Retrain the model using the updated dataset.


  1. The process is repeated, with the model iteratively selecting uncertain data points, querying for their labels, and updating its knowledge.
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