What is the standard strategy for honest assessment of model performance in predictive modeling?

The standard strategy for assessing model performance in predictive modeling is data splitting. This approach involves dividing the dataset into at least two distinct subsets: a training set and a validation (or test) set. The training set is used to build the predictive model, while the validation set is reserved for evaluating its performance.

By using different data for training and testing, practitioners can evaluate how well the model generalizes to unseen data. This helps to mitigate issues like overfitting, where a model performs exceptionally well on the training data but poorly on new data.

Moreover, data splitting allows you to gauge various performance metrics, such as accuracy, precision, recall, and F1 score, which are crucial to understanding the model's strengths and weaknesses in a real-world scenario. This strategy can also be extended to include further techniques such as k-fold cross-validation, which improves reliability in performance assessment by using multiple splits.

While data refinement, convergence, and reduction might involve preprocessing and optimizing the model or the data set, they do not focus on the honest evaluation of model performance in the way that data splitting does.