Un peso es un valor numérico aprendido que determina con qué fuerza una entrada, característica o conexión influye en la salida de un modelo. Durante el entrenamiento, un modelo ajusta pesos para reducir el error y mejorar las predicciones.
A value that a model multiplies by another value. Training is the process of determining a model's ideal weights; inference is the process of using those learned weights to make predictions. Imagine a linear model with two features. Suppose that training determines the following weights (and bias): - The bias, b, has a value of 2.2 - The weight, w~1~ associated with one feature is 1.5. - The weight, w~2~ associated with the other feature is 0.4. Now imagine an example with the following feature values: - The value of one feature, x~1~, is 6. - The value of the other feature, x~2~, is 10. This linear model uses the following formula to generate a prediction, y': Therefore, the prediction is: If a weight is 0, then the corresponding feature doesn't contribute to the model. For example, if w~1~ is 0, then the value of x~1~ is irrelevant. See Linear regression in Machine Learning Crash Course for more information.