Next, we will combine the training (`grp=1`) and testing (`grp=0`) datasets into one dataframe and manually calculate some accuracy statistics:

`preds$error`: this is the absolute difference between the outcome (0,1) and the prediction. Recall that for a binary regression model, the prediction represents the probability that the event (diabetes) will occur.`preds$errorsqr`: this is the calculated squared error. This is done in order to remove the sign.`preds$correct`: in order to classify the probability into correct or not correct, we will compare the error to a`.5`cutoff. If the error was small (<-`.5`) we will call it correct, otherwise it will be considered not correct. This is a somewhat arbitrary cutoff, ...