14.8 Evaluating the Model
REVIEW Before deploying a model we will want to have some measure of confidence in the predictions. This is the role of evaluation—we evaluate the performance of a model to gain an expectation of how well the model will perform on new observations.
We evaluate a model by making predictions on observations that were
not used in building the model. These observations will need to have a
known outcome so that we can compare the model prediction against the
known outcome. This is the purpose of the
test dataset as explained
in Section 8.12.
head(predict_te) == head(actual_te)
##  TRUE TRUE TRUE FALSE TRUE TRUE
sum(head(predict_te) == head(actual_te))
##  5
sum(predict_te == actual_te)
##  27193
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