After Model Builder trains and selects the best model, you can move on to the Evaluate step, which shows you various output (like the best-performing algorithm, how many models were explored, and the ML task, in this case binary classification) and lets you try out the model in the UI.
You can toggle between the Overview and Details to see more information about the training session and the top models explored (up to 5), including several evaluation metrics for each of those top models.
You can make predictions on sample input in the Try your model section. The textbox is pre-filled with the first line of data from your dataset, but you can change the input and hit Predict to try out different Sentiment predictions.
After evaluating and trying out your model, move on to the Code step.
After the ML.NET CLI selects the best model, it will display the Experiment Results, which shows you a summary of the exploration process, including how many models were explored in the given training time.
While the ML.NET CLI generates code for the highest performing model, it also displays the top models (up to 5) with the highest accuracy that it found in the given exploration time. It displays several evaluation metrics for those top models, including AUC, AUPRC, and F1-score, which you can learn more about here.