linear separability
[ˈlɪniər ˌsɛpərəˈbɪləti]
noun
separabilidade linear
1. In machine learning and geometry, the property of a dataset where two classes can be perfectly separated by a single linear decision boundary (hyperplane) in feature space
The dataset exhibits linear separability, allowing the perceptron algorithm to find a perfect classification boundary.
O conjunto de dados apresenta separabilidade linear, permitindo que o algoritmo perceptron encontre um limite de classificação perfeito.
2. The quality or condition of being separable using linear methods or linear transformations
Testing for linear separability is crucial before applying linear classifiers to your data.
Testar a separabilidade linear é crucial antes de aplicar classificadores lineares aos seus dados.
This is a specialized technical term used primarily in computer science, machine learning, and artificial intelligence fields. It is equally understood in both Brazilian and European Portuguese academic contexts. The term is rarely used in casual conversation and is confined to technical and academic discussions about data classification algorithms.
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