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Conformal prediction (CP) is a set of algo … Conformal prediction (CP) is a set of algorithms devised to assess the uncertainty of predictions produced by a machine learning model. CP algorithms do this by computing and comparing nonconformity measures (often referred to as α-values), of examples from the training set, and compare these with measure computed for examples from a test set. Conformal predictors can be divided into inductive and transductive. These mainly differ in their computational complexity and whether they can be applied to regression or classification tasks. Inductive algorithms train one or several machine learning models which are re-used for future test objects, and can be used for both classification and regression tasks, whereas transductive algorithms re-train the model for every test object, and can only be used for classification tasks. Conformal prediction requires a user-specified significance level for which the algorithm should produce its predictions. This significance level restricts the frequency of errors that the algorithm is allowed to make. For example, a significance level of 0.1 means that the algorithm can make at most 10% erroneous predictions. To meet this requirement, the output is a set prediction, instead of a point prediction produced by standard supervised machine learning models. For classification tasks, this means that predictions are not a single class, for example 'cat', but instead a set like {'cat', 'dog'}. Depending on how good is the underlying model (how well it can discern between cats, dogs and other animals) and the specified significance level, these sets can be smaller or larger. For regression tasks, the output is prediction intervals, where a smaller significance level (less allowed errors) produces wider intervals which are less specific, and vice versa – more allowed errors produces tighter prediction intervals.ors produces tighter prediction intervals.
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Conformal prediction (CP) is a set of algo … Conformal prediction (CP) is a set of algorithms devised to assess the uncertainty of predictions produced by a machine learning model. CP algorithms do this by computing and comparing nonconformity measures (often referred to as α-values), of examples from the training set, and compare these with measure computed for examples from a test set. Conformal predictors can be divided into inductive and transductive. These mainly differ in their computational complexity and whether they can be applied to regression or classification tasks. Inductive algorithms train one or several machine learning models which are re-used for future test objects, and can be used for both classification and regression tasks, whereas transductive algorithms re-train the model for every test object, and can only bedel for every test object, and can only be
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| rdfs:label |
Conformal prediction
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