1) In the United States, it is probably against the law to use a Random Forest for Marketing models.
A) False
2) Which of these modelling techniques is usually the easiest to interpret?
A) Decision Trees
3) Gradient Boosting models are easy to interpret.
A) False
4) Which of these modelling techniques alters the data in order to over sample records that it incorrectly classified?
A) Gradient Boosting
5) Random Forests and the Gradient Boosting models will usually be more accurate than Decision Tree models.
A) True
6) Which of these modelling techniques is not adversely affected by outliers?
A) All of these
7) Which of these modelling techniques trains many trees with each tree is built on a random subset of variables?
A) Random Forests
8) Which of these modelling techniques tends to use many small trees?
A) Gradient Boosting
9) In the United States, it is probably against the law to use a Gradient Boosting model for Marketing models.
A) False
10) A Gradient Boosting model is less sensitive to a small input change than a Decision Tree
A) True
11) Random Forests and the Gradient Boosting models will always be more accurate than Decision Tree models.
A) False
12) Random Forests are easy to interpret.
A) False
13) In the United States, it may be against the law to use a Gradient Boosting models for Credit or Auto Insurance models.
A) True
14) In the United States, it may be against the law to use a Random Forest for Credit or Auto Insurance models.
A) True
15) Which of these modelling techniques trains many trees with each tree is built on a random subset of records?
A) Random Forests
16) Which of these modelling techniques is usually the fastest to train?
A) Decision Trees
17) Which of these modelling techniques is usually the easiest to convert into IF-THEN-ELSE rules?
A) Decision Trees
18) A Random Forest is more sensitive to a small input change than a Decision Tree
A) False