Final answer:
When we use a model to predict the values of a validation set and compare them with the hidden true values, it is known as model evaluation or model validation.
Step-by-step explanation:
When we use a model to predict the values of a validation set and compare them with the hidden true values, it is known as model evaluation or model validation.
In this process, we apply the trained model to the validation set, which contains data that the model hasn't seen before. We then compare the predicted values from the model with the actual values in the validation set to assess the performance of the model.
By analyzing the differences between predicted and true values, we can determine how accurate our model is and make any necessary adjustments or improvements.If your hypothesis is correct, you would expect the observations to match the predictions made by the model.
This is particularly useful in scenarios like the third exam/final exam example, where one can predict the final exam grade based on the performance in the third exam using the best-fit line equation.