Evaluation in this case could mean a few different things.

Evaluation in this case could mean a few different things. If you are performing a bottom-up style recursive partitioning analysis, you should report on the error rate and variable selection. You might also consider alternative variable categorizations to improve your model.

If you are performing a top-down decision tree modeling exercise, what are the threshold values that cause the tree to flip? You should perform sensitivity analysis on the critical variables in your tree and report what those sensitivity analyses are telling you.

For either style of modeling, what makes your tree stronger? What breaks the model?

SAMPLE SOLUTION

The decision tree model being used is the top-down decision tree modelling. In the model, the student has the option of selecting either math or science both of which will be determined by the threshold set for the student’s performance. The top-down decision tree begins with the student whose performance will dictate on whether to select science or math as dictated by the aggregate points. A better aggregate point score in one will provide the student with a chance of picking that specific subject over the other.
There are threshold values that will make the tree to flip. For the science, the expected threshold is an AP score is an average of 3.2 while for math, the average AP score is 2.5. This implies that as a student, any score above the individual thresholds will…