El entrenamiento es el proceso de usar datos y un algoritmo de aprendizaje para determinar o mejorar los parámetros de un modelo. Durante el entrenamiento, el sistema ajusta valores internos como pesos, puntos de división o sesgos para que sus salidas se ajusten mejor a la tarea prevista.
The process of determining the ideal parameters (weights and biases) comprising a model. During training, a system reads in examples and gradually adjusts parameters. Training uses each example anywhere from a few times to billions of times. See Supervised Learning in the Introduction to ML course for more information.
Training is the process of presenting a set of data examples to an AI system in order to refine its model. The AI system can improve how it works by analyzing the training data, making predictions, and comparing those predictions against the actual outcome. Then, the AI system uses that information to tune the model to improve accuracy.
The process of identifying a model for a given training data set. For a linear model, this means finding the weights. For a tree, it involves identifying the split points.