Final answer:
After cross-validation, analyze the performance metrics, train the model on the full dataset, test it on a separate set, refine the model if needed, and move to deployment if the results are satisfactory.
Step-by-step explanation:
After performing cross-validation on a machine learning model, you should analyze the results to assess the model's performance. This typically involves looking at performance metrics such as accuracy, precision, recall, or F1 score, depending on the problem you're trying to solve.
You can also look for signs of model issues like overfitting or variance in the performance across the folds. If the model shows satisfactory results and generalizes well, you proceed to train it on the full dataset and then test it on a separate held-out test set to confirm performance metrics.
Finally, based on the performance on the test set, you refine the model if necessary, or you move on to the deployment phase where the model is put into production for practical use. It's important to remember that cross-validation is part of model tuning and validation, but it does not replace the need for an independent test set evaluation.