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}}</ref> In statistics literature, it is sometimes also called [[optimal experimental design]].<ref name="olsson">{{cite web | url=http://eprints.sics.se/3600/ | title=A literature survey of active machine learning in the context of natural language processing |series=SICS Technical Report T2009:06 | author=Olsson, Fredrik| date=April 2009 }}</ref> The information source is also called ''teacher'' or ''oracle''.
There are situations in which unlabeled data is abundant but manual labeling is expensive. In such a scenario, learning algorithms can actively query the user/teacher for labels. This type of iterative supervised learning is called active learning. Since the learner chooses the examples, the number of examples to learn a concept can often be much lower than the number required in normal supervised learning. With this approach, there is a risk that the algorithm is overwhelmed by uninformative examples. Recent developments are dedicated to multi-label active learning,<ref name="multi"/> hybrid active learning<ref name="hybrid"/> and active learning in a single-pass (on-line) context,<ref name="single-pass"/> combining concepts from the field of machine learning (e.g. conflict and ignorance) with adaptive, [[incremental learning]] policies in the field of [[online machine learning]]. Using active learning allows for faster development of a machine learning algorithm, when comparative updates would require a quantum or super computer.<ref>{{Cite journal |last=Novikov |first=Ivan |date=2021 |title=The MLIP package: moment tensor potentials with MPI and active learning
Large-scale active learning projects may benefit from [[crowdsourcing]] frameworks such as [[Amazon Mechanical Turk]] that include many [[human-in-the-loop|humans in the active learning loop]].
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*'''Querying from diverse subspaces or partitions''':<ref name="shubhomoydas_github"/> When the underlying model is a forest of trees, the leaf nodes might represent (overlapping) partitions of the original [[feature (machine learning)|feature space]]. This offers the possibility of selecting instances from non-overlapping or minimally overlapping partitions for labeling.
*'''Variance reduction''': label those points that would minimize output variance, which is one of the components of error.
*'''[[Conformal prediction]]''': predicts that a new data point will have a label similar to old data points in some specified way and degree of the similarity within the old examples is used to estimate the confidence in the prediction.<ref>{{Cite journal|last1=Makili|first1=Lázaro Emílio|last2=Sánchez|first2=Jesús A. Vega|last3=Dormido-Canto|first3=Sebastián|date=2012-10-01|title=Active Learning Using Conformal Predictors: Application to Image Classification|journal=Fusion Science and Technology|volume=62|issue=2|pages=347–355|doi=10.13182/FST12-A14626|bibcode=2012FuST...62..347M |s2cid=115384000|issn=1536-1055}}</ref>
*'''Mismatch-first farthest-traversal''': The primary selection criterion is the prediction mismatch between the current model and nearest-neighbour prediction. It targets on wrongly predicted data points. The second selection criterion is the distance to previously selected data, the farthest first. It aims at optimizing the diversity of selected data.<ref name='zhaos' />
*'''User Centered Labeling Strategies:''' Learning is accomplished by applying dimensionality reduction to graphs and figures like scatter plots. Then the user is asked to label the compiled data (categorical, numerical, relevance scores, relation between two instances.<ref name=":3">{{Cite journal |last1=Bernard |first1=Jürgen |last2=Zeppelzauer |first2=Matthias |last3=Lehmann |first3=Markus |last4=Müller |first4=Martin |last5=Sedlmair |first5=Michael |date=June 2018 |title=Towards User-Centered Active Learning Algorithms |url= |journal=Computer Graphics Forum |volume=37 |issue=3 |pages=121–132 |doi=10.1111/cgf.13406 |s2cid=51875861 |issn=0167-7055}}</ref>
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