Active learning is a type of machine learning approach that is focused on optimizing the use of labeled data by actively selecting which samples should be labeled next. In other words, instead of randomly selecting data points to be labeled, active learning algorithms try to select the most informative samples that can improve the performance of the model with the least amount of labeling effort.
In active learning, the algorithm is trained on a small set of labeled data, and then it selects a subset of data points from the unlabeled dataset that are most likely to improve model performance if they were labeled and added to the training set. The algorithm selects the samples that are most uncertain or informative. This selection process may involve sampling algorithms such as uncertainty sampling, query-by-committee, and density-based sampling.
Uncertainty sampling selects the samples the model is least certain about their classification, so that the addition of their label can better inform the model about the difficult points. Query-by-committee uses samples that are difficult for a set of models with different initial random weights in order to increase the diversity of the sample with respect to model opinions. Density-based sampling focuses on discovering regions of high uncertainty in the data space.
By selecting the most informative samples, active learning greatly reduces the amount of labeled data required to achieve the same level of accuracy compared to passive learning approaches which blindly assume an equal degree of relevance across all samples.
Active learning can be extremely useful in scenarios where the acquisition of labeled data is expensive, time-consuming, or difficult to obtain. For example, in a medical diagnosis setting, labeling data can be expensive and can require consent from the patient. In such scenarios, active learning can be used to select the most informative cases for labeling, thus minimizing the time and monetary costs.
In summary, active learning is a promising machine learning approach that can significantly reduce the amount of labeled data required to achieve high accuracy in a model while minimizing the labeling cost.