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QUESTION 1

  1. The method that predicts a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency is :a.Cohesionb.Separationc.Regressiond.Correlation

5 points   

QUESTION 2


  1. Using tables given, calculate following calculate the Cost of each Model. Which one is true? (10 points)

    a.M2 has higher COSTb.They have the same COSTc.M1 has higher COSTd.We need more information

5 points   

QUESTION 3

  1. Assume, two attributes have a correlation of 0.02; what does this tell you about the relationship of the two attributes?a.When one decreases the other one increasesb.They have strong correlaionc.They are highly correlatedd.They have weak positive correlation

5 points   

QUESTION 4

  1. The problem that arises when you fit your model based on your training data is calleda.False Positive (FP)b.False Negative (FN)c.Overfittingd.Underfitting

5 points   

QUESTION 5

  1. The accuracy of a Classification Model is calculated using: a.Entire Datasetb.None of thesec.Testing setd.Training Set

5 points   

QUESTION 6

  1. Given two models of similar generalization errors, one should prefer the simpler model over the more complex model, is the definition ofa.Occam’s Razorb.Simple Model Theoryc.Basic Model Principled.Accuracy Models

5 points   

QUESTION 7

  1. Which one is the most common measure to evaluate K-Means Clustersa.Cohesionb.Separationc.Cluster Meand.SSE

5 points   

QUESTION 8

  1. The method where you reserve 2/3 for training and 1/3 for testing isa.Cross Validationb.Stratified Trainingc.Bootstrapd.Holdout

5 points   

QUESTION 9

  1. ___________________ measures how closely related are objects in a clustera.Cluster Mean b.Cluster Separationc.Cluster Centroid d.Cluster Cohesion

5 points   

QUESTION 10

  1. K-means isa.Centroid-based Hierarchical clusteringb.Medoid-based Partitional clustering approachc.Medoid-based Hierarchical clusteringd.Centroid -based Partitional clustering approach

5 points   

QUESTION 11

  1. For the tree given below: 
    What is the training error (optimistic error) for the parent:

    a.12/36b.5/36c.10/36d.24/36

5 points   

QUESTION 12

  1. For the tree given below: 
    What is the training error (optimistic error) for the children:

    a.5/36b.6/36c.24/36d.12/36

5 points   

QUESTION 13

  1. For the tree given below: 
    What is the training pessimistic error for the children (N=0.5):

    a.8/36b.6/36c.12/36d.26/36

10 points   

QUESTION 14

  1. K-means is :a.Centroid -based Partitional clustering approachb.Medoid-based Partitional clustering approachc.Medoid-based Hierarchical clusteringd.Centroid-based Hierarchical clustering

5 points   

QUESTION 15

  1. ___________________measures how closely related are objects in a cluster 
    ___________________measures how distinct or well-separated a cluster is from other clusters

    a.Cluster Cohesion – Cluster Separationb.Cluster Separation – Cluster Cohesionc.Cluster Similarity – Cluster Distanced.Cluster Distance – Cluster Similarity

5 points   

QUESTION 16

  1. Consider the training examples shown in below Table for a binary classification problem (10 points)

    Calculate the Gini value for the following attributes and answer the following questions:
    Which attribute is better, Gender, Car Type, or Shirt Size for the 1st split ? a.It does not matter. Algorithm chooses one randomly. b.Shirt Sizec.Genderd.Car Type

10 points   

QUESTION 17

  1. Consider the training examples shown in below Table for a binary classification problem (10 points)

    Calculate the Gini value for the following attributes and answer the following questions: Gender, Car Type, Shirt Size
    What is the right order of the Gini Values from the lowest to highest?a.Gender <  Shirt Size < Car Type b.Gender < Car Type < Shirt Size
    c.Car Type < Gender <  Shirt Size d.Shirt Size < Car Type < Gender