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. 

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Machine Learning 101 Practice Test: Decision Trees

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25 Questions

1. Given the table which shows the abstract details of players who play a particular game on various days according to the weather conditions. The standard deviation of players is 6.58. What is the Standard deviation reduction for Temperature?

5.681.892.340.9

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

It 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 nodeIt 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 done

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

It starts with a tree with a single leaf and assign this leaf a label according to a majority vote among all labels over the training setAmong all possible splits, it either choose the one that minimizes the gain and perform it, or choose not to split the leaf at allIt defines some gain measure that quantifies the improvement due to the splitIt performs a series of iterations and on each iteration, it examine the effect of splitting a single leaf

4. In a splitting rule at internal nodes of the tree based on thresholding the value of a single feature, it follows that a tree with k leaves can shatter a set of k instances.

Don't knowMaybeFalseTrue

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

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

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

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

7. Preference bias is also known as search bias.

FalseMaybeTrueDon't know

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

It cares about only the current splitIt divides the predictor space into distinct and non-overlapping regionsIt always looks for the best variable in the future splitIt divides the independent variables into distinct and non-overlapping regions

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

A Decision tree is also known as a classification treeEach element of the domain of the classification in decision tree is called a classIt cannot be used in data mining applications as it only classifies but not predicts anythingIt is a tree in which each internal node is labeled with an input feature

10. According to Occam’s Razor, which of the following statements is not favorable to short hypotheses?

A long hypothesis that fits the data might be a coincidenceA short hypothesis that fits the data is unlikely to be a coincidenceThere are many ways to define small set of hypothesesIt is good to use fewer short hypotheses than long hypotheses

11. Consider the example, number of corrected mis – classifications at a particular node, n'(t) = 15.5, and number of corrected mis – classifications for sub – tree, n'(T_t) = 12. N(t) is the number of training set examples at node t and it is equal to 35. Here the tree should be pruned.

TrueFalseMaybeDon't know

12. Pre pruning is also known as online pruning.

Don't knowTrueMaybeFalse

13. Given the entropy for a split, E_split = 0.39 and the entropy before the split, E_before = 1. What is the Information Gain for the split?

2.5610.390.61

14. In Classification trees the value obtained by terminal node in the training data is the mode of observations falling in that region.

FalseDon't knowMaybeTrue

15. Information Gain and Gini Index are the same.

Don't knowTrueFalseMaybe

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

It is not fast to computeIt has the advantage of being conciseIt is able to make few assumptions beyond normality of the responseIt works equally well with numerical or categorical predictors

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

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

18. What does the following figure represent?

Decision tree for ANDDecision tree for XORDecision tree for XNORDecision tree for OR

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

It has high complexityMore computational resources are required to implement Random Forest algorithmConstruction of Random forests are time-consuming than decision treesConstruction of Random forests are much easier than decision trees

20. Consider we have a set of data with 3 classes, and we have observed 20 examples of which the greatest number 15 is in class c. If we predict that all future examples will be in class c, what is the expected error rate without pruning?

0.150.050.220.17

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

TrueDon't knowMaybeFalse

22. Which of the following statements is not true about CART?

It is used for generating regression treeIt always uses Gini index as cost function to evaluate split in feature selectionIt is used for generating classification treeIt is used for binary classification

23. Post pruning is also known as backward pruning.

TrueMaybeDon't knowFalse

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

ID3CARTC4.5DBSCAN

25. Consider we have a set of data with 3 classes, and we have observed 20 examples of which the greatest number 15 is in class c. If we predict that all future examples will be in class c, what is the expected error rate using minimum error pruning?

0.5610.3040.4020.5y