Decision Tree Pruning MCQ

Test your knowledge with important Decision Tree Pruning MCQ and their applications. These MCQs are beneficial for competitive exams too. Explore 30 + more Decision Tree Pruning MCQs at Bissoy

• Which of the following is not a Post pruning technique?
  ক

  Reduced error pruning

  খ

  Error complexity pruning

  গ

  Minimum error pruning

  ঘ

  Chi – square pruning
• Which of the following statements is not true about Minimum number of objects pruning?
  ক

  Whenever 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 node

  খ

  Increasing no of objects increases accuracy of the dataset

  গ

  Increasing no of objects simplifies the tree

  ঘ

  The different ranges of the minimum no of objects are set for few examples and tested for accuracy
• Minimum number of objects pruning is a Post pruning technique.
  ক

  True

  খ

  False
• Which of the following statements is not a step in Pre pruning?
  ক

  Pre – pruning a decision tree involves using a termination condition to decide when to terminate some of the branches prematurely as the tree is generated

  খ

  When constructing the tree, some significant measures can be used to assess the goodness of a split

  গ

  High threshold results in oversimplified trees

  ঘ

  If partitioning the tuples at a node would result the split that falls below a pre specified threshold, then further partitioning of the given subset is expanded
• Pre pruning is also known as online pruning.
  ক

  True

  খ

  False
• Which of the following statements is not a step in Minimum error pruning?
  ক

  At each non leaf node in the tree, calculate expected error rate if that subtree is pruned

  খ

  Calculate the expected error rate for that node if subtree is not pruned

  গ

  If pruning the node leads to greater expected error rate, then keep the subtree

  ঘ

  If pruning the node leads to smaller expected error rate, then don’t prune it
• Minimum error pruning is a Top down approach.
  ক

  True

  খ

  False
• Which of the following statements is not an advantage of Reduced error pruning?
  ক

  Linear computational complexity

  খ

  Over pruning

  গ

  Simplicity

  ঘ

  Speed
• Which of the following statements is not true about Reduced error pruning?
  ক

  It is the simplest and most understandable method in decision tree pruning

  খ

  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 node

  গ

  If 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

  ঘ

  If 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
• Which of the following statements is not true about Post pruning?
  ক

  It begins by generating the (complete) tree and then adjust it with the aim of improving the classification accuracy on unseen instances

  খ

  It begins by converting the tree to an equivalent set of rules

  গ

  It would not overfit trees

  ঘ

  It converts a complete tree to a smaller pruned one which predicts the classification of unseen instances at least as accurately
• Post pruning is also known as backward pruning.
  ক

  True

  খ

  False
• Which of the following statements is not true about the pruning in the decision tree?
  ক

  When the decision tree is created, many of the branches will reflect anomalies in the training data due to noise

  খ

  The over fitting happens when the learning algorithm continues to develop hypothesis that reduce training set error at the cost of an increased test set errors

  গ

  It optimises the computational efficiency

  ঘ

  It reduces the classification accuracy
• Which of the following is not a Pruning technique?
  ক

  Cost based pruning

  খ

  Cost complexity pruning

  গ

  Minimum error pruning

  ঘ

  Maximum error pruning
• Which of the following statements is not true about Pruning?
  ক

  It removes the sections of the tree that provide little power to classify instances

  খ

  It is a technique in machine learning and search algorithms to reduce the size of the decision trees

  গ

  It increases the complexity of the final classifier

  ঘ

  It improves the predictive accuracy by the reduction of overfitting
• Pruning can only be performed by a bottom up walk on the decision tree.
  ক

  True

  খ

  False
• Pruning a tree reduces it to a much smaller tree.
  ক

  True

  খ

  False
• In the ID3 algorithm the returned tree will usually be very large.
  ক

  True

  খ

  False