stacking classifier
[/ˈstækɪŋ ˈklæsɪfaɪər/]
nounpl: stacking classifiers
classificador de empilhamento
1. A machine learning ensemble method that trains multiple base classifiers and uses their outputs as input features for a meta-classifier (or second-level classifier) to make final predictions
The stacking classifier combined predictions from decision trees, support vector machines, and neural networks to achieve superior accuracy.
O classificador de empilhamento combinou as previsões de árvores de decisão, máquinas de vetores de suporte e redes neurais para alcançar uma precisão superior.
2. An ensemble learning technique where base models feed their predictions to a higher-level model that learns how to optimally combine these predictions
We implemented a stacking classifier with logistic regression as the meta-learner and random forests as base estimators.
Implementamos um classificador de empilhamento com regressão logística como meta-aprendiz e florestas aleatórias como estimadores de base.
This is a technical term primarily used in data science, machine learning, and artificial intelligence contexts. It originated in machine learning research and is equally understood in Brazilian, Portuguese, and American technical communities. The term reflects the international nature of AI/ML research where English terminology is predominant, though Portuguese translations are increasingly standardized in Brazilian universities and companies.
Related Idioms & Phrases
stacking the deck (metaphorical use in referring to combining multiple models)
building on a foundation (describing how meta-classifiers build on base classifiers)
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