Which step in a typical machine learning process involves testing the solution on the test data?

In a typical machine learning process, there are several steps that include data preparation, model building, training, and evaluation. Once the model is trained on the training data, the next step is to test the solution on the test data to evaluate its performance. This step is called model evaluation, and it is essential to ensure that the model is not overfitting or underfitting the training data. The test data should be different from the training data to get an accurate evaluation of the model’s performance. The model’s performance is evaluated based on various metrics such as accuracy, precision, recall, F1 score, and others. The evaluation results help to fine-tune the model and make any necessary adjustments before deploying it into production.