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. 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

2. Suppose in a decision tree, we are making some simplifying assumptions that each instance is a vector of d bits (X = {0, 1}^d). Which of the following statements is not true about the above situation?

The hypothesis class becomes finite, but is still very largeAny classifier from {0, 1}d to {0, 1} can be represented by a decision tree with 2d leaves and depth of d + 1It thresholding the value of a single feature corresponds to a splitting rule of the form 1[xi=1] for some i = [d]Any classifier from {0, 1}d to {0, 1} can be represented by a decision tree with 2d+1 leaves and depth of d + 1

3. Splitting is the process of dividing a node into two or more sub-nodes.

MaybeFalseDon't knowTrue

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

Its hypothesis space introduces no additional biasID3 searches incompletely through the hypotheses space, from simple to complex hypotheses, until its termination condition is metIts hypothesis space introduces additional bias in its each iterationIts inductive bias is solely a consequence of the ordering of hypotheses by its search strategy

5. 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 coincidenceThere are many ways to define small set of hypothesesA short hypothesis that fits the data is unlikely to be a coincidenceIt is good to use fewer short hypotheses than long hypotheses

6. 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 classificationMulti classificationLinear RegressionMultiple Regression

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

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

8. What does it mean by the VC dimension of a class is 2^d?

The number of examples need to PAC learn the hypothesis class grows with 2d+1The number of examples need to PAC learn the hypothesis class grows with 2dThe number of examples need to PAC learn the hypothesis class grows with 2d-1The number of examples need to PAC learn the hypothesis class grows with 2d+1

9. Which of the following is represented by the below figure?

Classification treeSupport vector machineRegression treeRandom forest

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

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

11. 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.

MaybeFalseDon't knowTrue

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

TrueFalseDon't knowMaybe

13. Which of the following statements is not true about Minimum number of objects pruning?

The different ranges of the minimum no of objects are set for few examples and tested for accuracyIncreasing no of objects simplifies the treeWhenever the split is made which yields a child leaf that represents less than minobj from the data set, the parent node and children node are compressed to a single nodeIncreasing no of objects increases accuracy of the dataset

14. Post pruning is also known as backward pruning.

TrueDon't knowMaybeFalse

15. Which of the following statements is not true about a splitting rule at internal nodes of the tree based on thresholding the value of a single feature?

It move to the right or left child of the node on the basis of 1[xi < ϑ], where ϑ ∈ R is the thresholdIt move to the right or left child of the node on the basis of 1[xi < ϑ], where i ∈ [d] is the index of the relevant featureHere a decision tree splits the instance space, X = Rd, into cells, where each leaf of the tree corresponds to one cellSplits based on thresholding the value of a single feature are also known as multivariate splits

16. Which of the following statements is true about Candidate elimination?

Its search strategy introduces no additional biasCandidate elimination searches the hypotheses completely, and finding every hypothesis consistent with the training dataIts inductive bias is solely a consequence of the expressive power of its hypothesis representationIts inductive bias is solely a consequence of the ordering of hypotheses by its search strategy

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

It is the simplest and most understandable method in decision tree pruningIt 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 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 doneIf 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

18. Which of the following statements are not true about Inductive bias in ID3?

ID3 has preference of long trees with high information gain attributes far away from the rootID3 has preference for certain hypotheses over others, with no hard restriction on the hypotheses spaceID3 has preference of short trees with high information gain attributes near the rootIt is the set of assumptions that along with the training data justify the classifications assigned by the learner to future instances

19. Decision tree uses the inductive learning machine learning approach.

TrueFalseDon't knowMaybe

20. Which of the following statements is not true about Classification trees?

It labels, records, and assigns variables to discrete classesIt will always looks for the best variable available in the future splits for a better treeIt is built through a process known as binary recursive partitioningIt can also provide a measure of confidence that the classification is correct

21. 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

22. Pre pruning is also known as online pruning.

FalseDon't knowTrueMaybe

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

Among all possible splits, it either choose the one that minimizes the gain and perform it, or choose not to split the leaf at allIt 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 setIt 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

24. Which of the following statements are not true about the Classification trees?

The leaves represent class labelsThe target variable can take real numbersThe target variable can take a discrete set of valuesThe branches represent conjunctions of features

25. 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.

FalseTrueMaybeDon't know