Decision Trees MCQ

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• Preference bias is also known as search bias.
  ক

  True

  খ

  False
• Preference bias is more desirable than a restriction bias.
  ক

  True

  খ

  False
• Which of the following statements is true about Candidate elimination?
  ক

  Candidate elimination searches the hypotheses completely, and finding every hypothesis consistent with the training data

  খ

  Its inductive bias is solely a consequence of the ordering of hypotheses by its search strategy

  গ

  Its inductive bias is solely a consequence of the expressive power of its hypothesis representation

  ঘ

  Its search strategy introduces no additional bias
• Which of the following statements is not true about ID3?
  ক

  ID3 searches incompletely through the hypotheses space, from simple to complex hypotheses, until its termination condition is met

  খ

  Its inductive bias is solely a consequence of the ordering of hypotheses by its search strategy

  গ

  Its hypothesis space introduces additional bias in its each iteration

  ঘ

  Its hypothesis space introduces no additional bias
• Which of the following statements are not true about Inductive bias in ID3?
  ক

  It is the set of assumptions that along with the training data justify the classifications assigned by the learner to future instances

  খ

  ID3 has preference of short trees with high information gain attributes near the root

  গ

  ID3 has preference for certain hypotheses over others, with no hard restriction on the hypotheses space

  ঘ

  ID3 has preference of long trees with high information gain attributes far away from the root
• According to Occam’s Razor, which of the following statements is not favorable to short hypotheses?
  ক

  It is good to use fewer short hypotheses than long hypotheses

  খ

  A short hypothesis that fits the data is unlikely to be a coincidence

  গ

  A long hypothesis that fits the data might be a coincidence

  ঘ

  There are many ways to define small set of hypotheses
• Which of the following statements is not true about the Inductive bias in the decision tree?
  ক

  It is harder to define because of heuristic search

  খ

  Trees that place high information gain attributes close to the root are preferred

  গ

  Trees that place high information gain attributes far away from the root are preferred

  ঘ

  Shorter trees are preferred over longer ones
• Inductive bias is also known as learning bias.
  ক

  True

  খ

  False
• If the original number of real valued features is d and the number of examples is m, then which of the following statements is not true?
  ক

  The number of constructed binary features becomes dm

  খ

  Calculating the Gain of each feature might take O(dm2) operations

  গ

  With more improved implementation the run time can be reduced to O(dmlog(m))

  ঘ

  The constructed binary features are dm
• Which of the following statements is not true about reducing a real valued feature problem into binary feature?
  ক

  It utilizes the threshold based splitting rules

  খ

  Once the binary features are constructed the ID3 algorithm can be easily applied

  গ

  After ID3 applied it is easy to verify that there exists a decision tree with different training error

  ঘ

  After ID3 applied it is easy to verify that there exists a decision tree with same number of nodes
• Real valued features problems in decision trees cannot be solved using ID3 algorithm.
  ক

  True

  খ

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

  True

  খ

  False
• Which of the following statements is not true about Decision trees?
  ক

  It builds classification models in the form of a tree structure

  খ

  It builds regression models in the form of a tree structure

  গ

  The final result is a tree with decision nodes and leaf nodes

  ঘ

  It never breaks down a dataset into smaller subsets with increase in depth of tree
• Information Gain and Gini Index are the same.
  ক

  True

  খ

  False
• Which of the following statements is not an objective of Information Gain?
  ক

  It tries to determine which attribute in a given set of training feature vectors is most useful for discriminating between the classes to be learned

  খ

  Decision Trees algorithm will always tries to minimize Information Gain

  গ

  It is used to decide the ordering of attributes in the nodes of a decision tree

  ঘ

  Information Gain of certain event is the discrepancy of the amount ofinformation before someone observes that event and the amount after observation
• Given the entropy for a split, Esplit = 0.39 and the entropy before the split, Ebefore = 1. What is the Information Gain for the split?
  ক

  1

  খ

  0.39

  গ

  0.61

  ঘ

  2.56
• Which of the following statements is not true about Information Gain?
  ক

  It is a gain measure that is used in the ID3 algorithms

  খ

  It is the difference between the entropy of the label before and after the split

  গ

  It is based on the decrease in entropy after a data-set is split on an attribute

  ঘ

  Constructing a decision tree is all about finding attribute that returns the lowest information gain
• Which of the following statements is not true about the ID3 algorithm?
  ক

  It is used to generate a decision tree from a dataset

  খ

  It begins with the original set S as the root node

  গ

  On 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 attribute

  ঘ

  Finally it selects the attribute which has the largest entropy value
• Which of the following is not a Decision tree algorithm?
  ক

  ID3

  খ

  C4.5

  গ

  DBSCAN

  ঘ

  CART
• Practical decision tree learning algorithms are based on heuristics.
  ক

  True

  খ

  False
• Minimum description length (MDL) principle is used to avoid overfitting in decision trees.
  ক

  True

  খ

  False
• 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.
  ক

  False

  খ

  True
• 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? , where i ∈ is the index of the relevant feature b) It move to the right or left child of the node on the basis of 1, where ϑ ∈ R is the threshold c) Here a decision tree splits the instance space, X = Rd, into cells, where each leaf of the tree corresponds to one cell d) Splits based on thresholding the value of a single feature are also known as multivariate splits
  ক

  It move to the right or left child of the node on the basis of 1, where i ∈ is the index of the relevant feature

  খ

  xi < &vartheta;

  গ

  xi < &vartheta;

  ঘ

  xi < &vartheta;
• Which of the following statements is not true about the Decision tree?
  ক

  It can be applied on binary classification problems only

  খ

  It is a predictor that predicts the label associated with an instance by traveling from a root node of a tree to a leaf

  গ

  At each node, the successor child is chosen on the basis of a splitting of the input space

  ঘ

  The splitting is based on one of the features or on a predefined set of splitting rules
• Which of the following are the advantage/s of Decision Trees?
  ক

  Possible Scenarios can be added

  খ

  Use a white box model, If given result is provided by a model

  গ

  Worst, best and expected values can be determined for different scenarios

  ঘ

  All of the mentioned
• End Nodes are represented by __________
  ক

  Disks

  খ

  Squares

  গ

  Circles

  ঘ

  Triangles
• Chance Nodes are represented by __________
  ক

  Disks

  খ

  Squares

  গ

  Circles

  ঘ

  Triangles
• Decision Nodes are represented by ____________
  ক

  Disks

  খ

  Squares

  গ

  Circles

  ঘ

  Triangles
• Choose from the following that are Decision Tree nodes?
  ক

  Decision Nodes

  খ

  End Nodes

  গ

  Chance Nodes

  ঘ

  All of the mentioned
• Decision Trees can be used for Classification Tasks.
  ক

  True

  খ

  False