Machine learningMachine learning
Voting Ensemble
A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.
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Sources
- Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
- Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. In J. Kittler & F. Roli (Eds.), Multiple Classifier Systems (MCS 2000), Lecture Notes in Computer Science, vol 1857, pp. 1–15. Springer. DOI: 10.1007/3-540-45014-9_1 ↗
Related methods
Referenced by
Active learning Stacking ensembleActive Learning Voting EnsembleAssociation RulesBayesian BaggingBayesian Stacking EnsembleBoostingEnsemble Active LearningEnsemble Association RulesEnsemble Autoencoder Anomaly DetectionEnsemble Decision TreeEnsemble Federated LearningEnsemble Few-shot learningEnsemble Gaussian ProcessEnsemble Isolation ForestEnsemble K-nearest neighborsEnsemble Linear RegressionEnsemble Logistic RegressionEnsemble Metric LearningEnsemble Naive BayesEnsemble One-class SVMEnsemble Online LearningEnsemble Semi-supervised LearningEnsemble Support Vector MachineEnsemble Transfer LearningExplainable Voting EnsembleOnline Voting EnsembleRegularized Stacking EnsembleRobust BaggingRobust Voting EnsembleSemi-supervised Voting Ensemble