Active learning is a machine learning technique in which a machine learning model asks a user to manually label a small selection of training data to improve its performance. Instead of waiting for a large set of labelled data to be available for training, the active learning model uses a sample selection strategy to choose which data to request for labelling.
Sample selection is based on the model's degree of uncertainty about a sample, meaning that the model chooses samples that it believes are more difficult to classify. After the user labels these samples, the model is trained on the updated dataset and repeats the process.
Active learning is particularly useful in situations where manual labelling of data may be costly or difficult to obtain. For example, in medical image classification, it can be difficult to obtain large amounts of labelled data, but active learning can help improve model accuracy with careful selection of samples for labelling.
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