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probabilistic classifiers

A probabilistic classifier is a machine learning model that predicts a probability distribution over a set of possible classes for a given input, rather than merely assigning a single discrete class label. By quantifying the likelihood associated with each potential category, these models provide an explicit measure of uncertainty or confidence in their predictions. This probabilistic output enables practitioners to establish custom decision thresholds based on the relative costs of misclassification, rank instances by certainty, and seamlessly integrate predictions into broader statistical and decision-theoretic workflows. Common examples of probabilistic classifiers include logistic regression, naive Bayes classifiers, Gaussian processes, and neural networks utilizing softmax output functions.

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Heterogeneous Uncertainty Sampling for Supervised Learning

Heterogeneous Uncertainty Sampling for Supervised Learning

David D. Lewis, Jason Catlett

OrganizationsAT&T Bell Laboratories

Why you should read this

Demonstrates that using a fast probabilistic classifier to actively select training instances for a more complex C4.5 rule induction model achieves lower error rates on text categorization tasks than random sampling sets ten times larger.

Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program). Despite being chosen by this heterogeneous approach, the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger.

Added

2026-09-25