The Ultimate Guide to Model Retraining - KDnuggets

Machine learning models are trained by learning a mapping between a set of input features and an output target. Typically, this mapping is learned by optimizing some cost function to minimize prediction error. Once the optimal model is found, it's released out into the wild with the goal of generating accurate predictions on future unseen data. Depending on the problem, these new data examples may be generated from user interactions, scheduled processes, or requests from other software systems. Ideally, we hope that our models predict these future instances as accurately as the data used during the training process.