Also known as: Out-of-bag Evaluation (OOB Evaluation) · OOB Evaluation · Out-of-bag Evaluation OOB Evaluation · Out of bag Evaluation [OOB Evaluation]
La evaluación out-of-bag es un método para estimar el rendimiento de un bosque de decisión usando ejemplos no muestreados para entrenar un árbol determinado. Proporciona una aproximación computacionalmente eficiente a la validación cruzada porque cada árbol puede probarse con sus propios ejemplos reservados.
A mechanism for evaluating the quality of a decision forest by testing each decision tree against the examples not used during training of that decision tree. For example, in the following diagram, notice that the system trains each decision tree on about two-thirds of the examples and then evaluates against the remaining one-third of the examples. !A decision forest consisting of three decision trees. One decision tree trains on two-thirds of the examples and then uses the remaining one-third for OOB evaluation. A second decision tree trains on a different two-thirds of the examples than the previous decision tree, and then uses a different one-third for OOB evaluation than the previous decision tree. Out-of-bag evaluation is a computationally efficient and conservative approximation of the cross-validation mechanism. In cross-validation, one model is trained for each cross-validation round (for example, 10 models are trained in a 10-fold cross-validation). With OOB evaluation, a single model is trained. Because bagging withholds some data from each tree during training, OOB evaluation can use that data to approximate cross-validation. See Out-of-bag evaluation in the Decision Forests course for more information.