gs gs126 Decision Trees - Decision Tree Pruning - Quiz No.1

gs gs126 Neural Networks Quiz

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Question 1: Which of the following statements is not an advantage of Reduced error pruning?

Linear computational complexity

Over pruning

Simplicity

Speed

Question 2: Minimum error pruning is a Top down approach.

True

False

Question 3: Which of the following statements is not a step in Minimum error pruning?

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

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

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

If pruning the node leads to smaller expected error rate, then don’t prune it

Question 4: Pre pruning is also known as online pruning.

True

False

Question 5: Which of the following statements is not a step in Pre pruning?

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

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

High threshold results in oversimplified trees

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

Question 6: Minimum number of objects pruning is a Post pruning technique.

True

False

Question 7: Which of the following statements is not true about Minimum number of objects pruning?

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

Increasing no of objects increases accuracy of the dataset

Increasing no of objects simplifies the tree

The different ranges of the minimum no of objects are set for few examples and tested for accuracy

Question 8: Which of the following is not a Post pruning technique?

Reduced error pruning

Error complexity pruning

Minimum error pruning

Chi – square pruning

Question 9: Which of the following is not a Post pruning technique?

Pessimistic error pruning

Iterative growing and pruning

Reduced error pruning

Early stopping pruning

Question 10: 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?

0.304

0.5y

0.402

0.561

Question 11: 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?

0.22

0.17

0.15

0.05

Question 12: 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.

True

False

Question 13: Which of the following statements is not an advantage of Reduced error pruning?

Linear computational complexity

Over pruning

Simplicity

Speed

Question 14: Minimum error pruning is a Top down approach.

True

False

Question 15: Which of the following statements is not a step in Minimum error pruning?

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

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

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

If pruning the node leads to smaller expected error rate, then don’t prune it

Question 16: Pre pruning is also known as online pruning.

True

False

Question 17: Which of the following statements is not a step in Pre pruning?

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

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

High threshold results in oversimplified trees

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

Question 18: Minimum number of objects pruning is a Post pruning technique.

True

False

Question 19: Which of the following statements is not true about Minimum number of objects pruning?

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

Increasing no of objects increases accuracy of the dataset

Increasing no of objects simplifies the tree

The different ranges of the minimum no of objects are set for few examples and tested for accuracy

Question 20: Which of the following is not a Post pruning technique?

Reduced error pruning

Error complexity pruning

Minimum error pruning

Chi – square pruning

Question 21: Which of the following is not a Post pruning technique?

Pessimistic error pruning

Iterative growing and pruning

Reduced error pruning