It’s time to put what you’ve learned to the test, get 4 questions right to pass this unit.
1.
Which parameter of the Hybrid Gradient Boosting Tree (HGBT) algorithm controls the maximum depth of each tree?
Choose the correct answer.
2.
What is one of the key advantages of using the Hybrid Gradient Boosting Tree (HGBT) algorithm for regression tasks?
Choose the correct answer.
3.
What does R2 (R squared) represent in a regression model?
Choose the correct answer.
4.
Which of the following are valid scenarios for using the Hybrid Gradient Boosting Tree (HGBT) algorithm in SAP HANA for regression?
There are three correct answers.
5.
Imagine you are working on improving a machine learning model and decide to re-train it. After re-training, you notice a change in the feature importance scores. What does a change in feature importance scores indicate?