posteriorvalidació
Posterior validation is a crucial step in the development and deployment of any predictive model. It refers to the process of evaluating a model's performance after it has been trained and, in some cases, already used to make predictions on new, unseen data. This contrasts with prior validation (or internal validation), which uses a portion of the training data to assess performance during the model development phase.
The primary goal of posterior validation is to confirm whether the model's predictions are accurate and reliable
Posterior validation is essential for several reasons. It helps identify potential issues such as overfitting, where