gs gs126 Decision Trees - Decision Trees – Gain Measure Implementation - Quiz No.1
gs gs126 Neural Networks Quiz
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Question 1: Which of the following statements is not true about the Decision tree?
A Decision tree is also known as a classification tree
Each element of the domain of the classification in decision tree is called a class
It is a tree in which each internal node is labeled with an input feature
It cannot be used in data mining applications as it only classifies but not predicts anything
Question 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 set
It performs a series of iterations and on each iteration, it examine the effect of splitting a single leaf
It defines some gain measure that quantifies the improvement due to the split
Among all possible splits, it either choose the one that minimizes the gain and perform it, or choose not to split the leaf at all
Question 4: Which of the following is not a Decision tree algorithm?
ID3
C4.5
DBSCAN
CART
Question 5: Which of the following statements is not true about the ID3 algorithm?
It is used to generate a decision tree from a dataset
It begins with the original set S as the root node
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
Finally it selects the attribute which has the largest entropy value
Question 6: Which of the following statements is not true about Information Gain?
It is a gain measure that is used in the ID3 algorithms
It is the difference between the entropy of the label before and after the split
It is based on the decrease in entropy after a data-set is split on an attribute
Constructing a decision tree is all about finding attribute that returns the lowest information gain
Question 7: Which of the following statements is not true about Information Gain?
It is the addition in entropy by transforming a dataset
It is calculated by comparing the entropy of the dataset before and after a transformation
It is often used in training decision trees
It is also known as Kullback-Leibler divergence
Question 8: Which of the following statements is not true about Information Gain?
It is the amount of information gained about a random variable or signal from observing another random variable
It tells us how important a given attribute of the feature vectors is
It implies how much entropy we removed
Higher Information Gain implies less entropy removed
Question 10: Which of the following statements is not an objective of Information Gain?
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
Decision Trees algorithm will always tries to minimize Information Gain
It is used to decide the ordering of attributes in the nodes of a decision tree
Information Gain of certain event is the discrepancy of the amount ofinformation before someone observes that event and the amount after observation
Question 12: Which of the following statements is not true about Information Gain?
It is used to determine which feature/attribute gives us the maximum information about a class
It is based on the concept of entropy, which is the degree of impurity or disorder
It aims to reduce the level of entropy starting from the root node to the leave nodes
It is often promote the level of entropy starting from the root node to the leave nodes
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