Making Predictions in Multivariate Logistic Regression

So the model evaluation on the train set is complete and the model seems to be doing a decent job. You saw two views of the evaluation metrics - one was the sensitivity-specificity view, and the other was the precision-recall view. You can choose any of the metrics you like; it is completely up to you. In this session, we will go forward with the sensitivity-specificity view of things and make predictions based on the 0.3 cut-off that we decided earlier.

Note: At 2:45, Rahim mistakenly says, "On the main model we had accuracy about 79%. Please note that he said this by mistake. It should be 77% as you had calculated.

The metrics seem to hold on the test dataset as well. So, it looks like you have created a decent model for the churn dataset as the metrics are decent for both the training and test datasets.

You can also take the cutoff you got from the precision-recall tradeoff curve and try making predictions based on that on your own.