Quiz on important decision trees concepts including decision tree pruning, inductive bias, classification trees, regression trees, and the powerful Random Forest algorithm.   Decision trees are a type of machine learning algorithm that split a dataset based on specific parameters until a final decision is made. They are one of the most easily explainable types of machine learning models.  Here are some basics about decision trees: Pruning: A technique that simplifies decision trees by reducing the rules. This helps to avoid complexity and improves accuracy. Splitting: Decision trees... Show more

Quiz on important decision trees concepts including decision tree pruning, inductive bias, classification trees, regression trees, and the powerful Random Forest algorithm.
 

Decision trees are a type of machine learning algorithm that split a dataset based on specific parameters until a final decision is made. They are one of the most easily explainable types of machine learning models. 

Here are some basics about decision trees:
Pruning: A technique that simplifies decision trees by reducing the rules. This helps to avoid complexity and improves accuracy.
Splitting: Decision trees split on different nodes until an outcome is obtained.
Parent and child nodes: When a node gets divided further, that node is termed as a parent node. The divided nodes or the sub-nodes are termed as a child node of the parent node.
Supervised machine learning algorithms: Decision trees are used in both classification and regression predictive modeling.
Hyperparameter tuning: An essential part of the machine-learning process that involves optimizing the model's performance by fine-tuning its hyperparameters.
Overfitting: The biggest issue of decision trees in machine learning. Overfitting causes high variance in outputs, which causes errors in the final decisions and inaccuracy in results. 

Show less

Machine Learning 101 Practice Test: Decision Trees

Time left 00:00

25 Questions

1. Practical decision tree learning algorithms are based on heuristics.

TrueDon't knowMaybeFalse

2. Consider the figure. If person A starts driving at 8:30 AM and there are no other vehicles on the road, and another person B starts driving at 10 AM and there is an accident on the road, what will be the commute time of A and B respectively?

SHORT, LONGSHORT, SHORTLONG, SHORTLONG, LONG

3. Categorical Variable Decision tree has a categorical target variable.

Don't knowMaybeTrueFalse

4. Which of the following is not a Decision tree algorithm?

ID3DBSCANC4.5CART

5. Which of the following statements is not true about Pruning?

It is a technique in machine learning and search algorithms to reduce the size of the decision treesIt improves the predictive accuracy by the reduction of overfittingIt increases the complexity of the final classifierIt removes the sections of the tree that provide little power to classify instances

6. Which of the following statements is not true about the Regression trees?

The root node holds the final prediction valueThe terminal nodes of the tree contain the predicted output variable valuesRegression tree is a variant of decision trees, designed to approximate real-valued functionsThe general regression tree building methodology allows input variables to be a mixture of continuous and categorical variables

7. Linear regression is a global model.

TrueFalseDon't knowMaybe

8. Which of the following statements is not true about Information Gain?

It is the addition in entropy by transforming a datasetIt is also known as Kullback-Leibler divergenceIt is often used in training decision treesIt is calculated by comparing the entropy of the dataset before and after a transformation

9. Pre pruning is also known as online pruning.

FalseDon't knowTrueMaybe

10. Which of the following statements is not true about the Regression trees?

It needs more data than other regression techniquesIt gives crude predictions when it is sensitive to the particular dataIt gives processed predictions when it is sensitive to the particular dataIt is especially sensitive to the particular data used to build the tree

11. Which of the following statements is not true about the Random forests?

It outputs the class that is the mode of the classesIt outputs the mean prediction of the individual treesIt operates by constructing a multitude of decision trees at training timeIt is an ensemble learning method for classification only

12. Which of the following statements is not true about Reduced error pruning?

It is the simplest and most understandable method in decision tree pruningIf the error rate of the new tree would be greater than that of the original tree and that subtree contains no subtree with the same property, then subtree is replaced by leaf node, means pruning is doneIt considers each of the decision nodes in the tree to be candidates for pruning, consist of removing the subtree rooted at that node, making it a leaf nodeIf the error rate of the new tree would be equal to or smaller than that of the original tree and that subtree contains no subtree with the same property, then subtree is replaced by leaf node

13. In the ID3 algorithm the returned tree will usually be very large.

TrueMaybeDon't knowFalse

14. Which of the following statements is not an objective of Information Gain?

Decision Trees algorithm will always tries to minimize Information GainIt tries to determine which attribute in a given set of training feature vectors is most useful for discriminating between the classes to be learnedIt is used to decide the ordering of attributes in the nodes of a decision treeInformation Gain of certain event is the discrepancy of the amount ofinformation before someone observes that event and the amount after observation

15. A Regression tree is built through a process known as binary recursive partitioning.

MaybeDon't knowTrueFalse

16. Which of the following statements is not true about Random forests?

Construction of Random forests are time-consuming than decision treesMore computational resources are required to implement Random Forest algorithmConstruction of Random forests are much easier than decision treesIt has high complexity

17. Which of the following statements is not true about Random forests?

It works well for a large range of data items than a single decision treeIt has less variance than single decision treeScaling of data required in random forest algorithmRandom forests are very flexible and possess very high accuracy

18. There is a direct relationship between the number of trees in the random forest and the results.

TrueFalseMaybeDon't know

19. Which of the following statements is not true about the ID3 algorithm?

It is used to generate a decision tree from a datasetOn each iteration of the algorithm, it iterates through every unused attribute of the set S and calculates the entropy or the information gain of that attributeFinally it selects the attribute which has the largest entropy valueIt begins with the original set S as the root node

20. Which of the following statements is not true about Information Gain?

It is the difference between the entropy of the label before and after the splitIt is a gain measure that is used in the ID3 algorithmsIt is based on the decrease in entropy after a data-set is split on an attributeConstructing a decision tree is all about finding attribute that returns the lowest information gain

21. Which of the following statements is not true about Information Gain?

It implies how much entropy we removedIt is the amount of information gained about a random variable or signal from observing another random variableHigher Information Gain implies less entropy removedIt tells us how important a given attribute of the feature vectors is

22. Which of the following statements is not true about reducing a real valued feature problem into binary feature?

After ID3 applied it is easy to verify that there exists a decision tree with different training errorAfter ID3 applied it is easy to verify that there exists a decision tree with same number of nodesIt utilizes the threshold based splitting rulesOnce the binary features are constructed the ID3 algorithm can be easily applied

23. Which of the following statements is not true about the Decision tree?

It is a predictor that predicts the label associated with an instance by traveling from a root node of a tree to a leafAt each node, the successor child is chosen on the basis of a splitting of the input spaceThe splitting is based on one of the features or on a predefined set of splitting rulesIt can be applied on binary classification problems only

24. Which of the following is expressed by the given equation Y = β₀ + β₁X + Ɛ which shows a real-valued dependent variable Y is modeled as function of a real-valued independent variable X plus noise?

Binary classificationLinear RegressionMulti classificationMultiple Regression

25. Which of the following statements is not an advantage of Reduced error pruning?

SimplicityLinear computational complexityOver pruningSpeed