4.3 Keeping an Eye on Healthcare Costs: The D2Hawkeye Story | 4.3 Keeping an Eye on Healthcare Costs: The D2Hawkeye Story | 4 Trees | The Analytics Edge | Sloan School of Management

Quick Question

In the previous video, we constructed two CART models. The first CART model, without the loss matrix, predicted bucket 1 for 78.6% of the observations in the test set. Did the second CART model, with the loss matrix, predict bucket 1 for more or fewer of the observations, and why?

According to the penalty matrix, some of the worst types of errors are to not predict bucket 1 when the actual cost bucket is bucket 1. Therefore, the model with the penalty matrix predicted bucket 1 more frequently.

According to the penalty matrix, some of the worst types of errors are to predict bucket 1 when the actual cost bucket is higher. Therefore, the model with the penalty matrix predicted bucket 1 more frequently.

Explanation

If you look at the classification matrix for the second CART model, we predicted bucket 1 less frequently. This is because, according to the penalty matrix, some of the worst types of errors are to predict bucket 1 when the actual cost bucket is higher.