MCQs On Decision Trees! #31

A quick knowledge check!

Hello Everyone,

Welcome to the 31st edition of my newsletter ML & AI Cupcakes!

The agenda of today’s newsletter is to have a quick knowledge check of your understanding of decision tree algorithm.

Decision tree algorithm is the basis for other popular ensembling algorithms like random forest and xgboost. If you want to understand the working of these ensembling algorithms, you first need to understand decision trees well.

So, make sure you keep strengthening your decision tree fundamentals.

Without any further delay, let’s begin the test.

Good luck!

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1. Which of the following is a node splitting critera used in decision trees?

   A) Information gain

   B) Gini impurity

   C) Variance reduction

   D) All of the above

2. Decision Trees can be used for ______ .

   A) Classification tasks only

   B) Regression tasks only

   C) Both classification and regression tasks

   D) Neither classification nor regression tasks

3. What is the general impact of increasing tree depth on overfitting?

   A) Increase overfitting

   B) Decreases overfitting

   C) No impact

4. Leaf nodes are also know as terminal nodes in a decision tree.

   A) True

   B) False

5. What is the gini index of a perfectly pure node?

   A) -1

   B) 0

   C) 0.5

   D) 1

6. What is the default node splitting criteria in DecisionTreeClassifier?

   A) Gini index

   B) Variance reduction

   C) Mean squared error

   D) Information gain

7. Can we change the default node splitting criteria in DecisionTreeClassifier?

   A) Yes

   B) No

8. What does entropy does in the context of a decision tree?

   A) Selects the depth of a tree

   B) Decides the number of leaf nodes in a tree

   C) Measures the impurity in an internal node

   D) None of the above

9. Which of the following is a type of decision tree algorithm?

   A) DBSCAN

   B) C4.5

   C) K-Means clustering

   D) ADASYN

10. What is the entropy of a pure node in a binary classification problem?

    A) -1

    B) 0

    C) 0.5

    D) 1

11. When is entropy value the highest for a binary classification problem with 20 observations?

    A) When there is only one class in the dataset

    B) When there is imbalance in the classes

    C) When both the classes have equal number of observations

    D) None of the above

12. Information gain and gini index have the same mathematical formula.

    A) True

    B) False

13. Information gain and entropy are same.

    A) True

    B) False

14. Which of the following is true while selecting features for splitting using gini index?

    A) The feature with lowest gini index is chosen for splitting

    B) The feature with highest gini index is chosen for splitting

15. Which of the following is true while selecting features for splitting using information gain?

    A) The feature with lowest information gain is chosen for splitting

    B) The feature with highest information gain is chosen for splitting

16. Which of the following is a parameter of DecisionTreeClassifier in scikit-learn?

    A) max_depth

    B) min_samples_split

    C) max_features

    D) All of the above

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Answers

1.D 2.C 3.A 4.A 5.B 6.A 7.A 8.C 9.B 10. B

11. C 12. B 13. B 14. A 15. B 16. D

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-Kavita