Regression validation

Regression validation is a process used to measure how well a predictive model can accurately predict outcomes. Basically, it helps us figure out how good our model is at making predictions. To do this, we take a dataset and split it into two groups: a training set and a validation set. The training set is used to "train" the model - which means we give it data to learn from, so that it can form a prediction. The validation set is used to test the model - which means we give the model data to see if it can accurately predict the outcomes. We then compare the model's prediction to the actual outcome - this gives us an idea of how accurate the model is.