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Found 13 papers, 1 papers with code
Invariance assumptions for class distribution estimation
Assumptions of invariance between the training joint distribution of features and labels and the test distribution can considerably facilitate this task.
Sparse joint shift in multinomial classification
We present new results on the transmission of SJS from sets of features to larger sets of features, a conditional correction formula for the class posterior probabilities under the target distribution, identifiability of SJS, and the relationship between SJS and covariate shift.
Factorizable Joint Shift in Multinomial Classification
Factorizable joint shift (FJS) was recently proposed as a type of dataset shift for which the complete characteristics can be estimated from feature data observations on the test dataset by a method called Joint Importance Aligning.
Class Prior Estimation under Covariate Shift: No Problem?
We show that in the context of classification the property of source and target distributions to be related by covariate shift may be lost if the information content captured in the covariates is reduced, for instance by dropping components or mapping into a lower-dimensional or finite space.
Minimising quantifier variance under prior probability shift
For the binary prevalence quantification problem under prior probability shift, we determine the asymptotic variance of the maximum likelihood estimator.
Calibrating sufficiently
When probabilistic classifiers are trained and calibrated, the so-called grouping loss component of the calibration loss can easily be overlooked.
Proving prediction prudence
We study how to perform tests on samples of pairs of observations and predictions in order to assess whether or not the predictions are prudent.
Confidence intervals for class prevalences under prior probability shift
Less attention has been paid to the construction of confidence and prediction intervals for estimates of class prevalences.
A plug-in approach to maximising precision at the top and recall at the top
For information retrieval and binary classification, we show that precision at the top (or precision at k) and recall at the top (or recall at k) are maximised by thresholding the posterior probability of the positive class.
Fisher consistency for prior probability shift
Lack of Fisher consistency could be used as a criterion to dismiss estimators that are unlikely to deliver precise estimates in test datasets under prior probability and more general dataset shift.
Does quantification without adjustments work?
In contrast, quantification has been defined as the task of determining the prevalences of the different sorts of class labels in a target dataset.
Exact fit of simple finite mixture models
A classical approach to this problem consists of fitting a mixture of the conditional score distributions observed last year to the current score distribution.
The Law of Total Odds
We quantify the bias of the total probability estimator of the unconditional class probabilities and show that the total odds estimator is unbiased.
