Question

1)Which of the following factors need to be taken into account while setting up an experiment?
(More than one answer may be correct)
a) Floor/Ceiling Effects
b) Order Effects
c) Sampling Bias

2) Select the correct equations.
TP - True Positive, TN - True Negative, FP - False Positive, FN - False Negative
(More than one answer may be correct)

a)Precision = TPTP+FP

b)Recall = FPTP+FP

c)Accuracy = TP+TNTP+TN+FP+FN

d)Recall = TPTP+FN


3)Which of the following measure best analyze the performance of a classifier?
a) Precision
b) Recall
c) accuracy
d) Time complexity
e) Depends on the application

4) For the ROC curve of True positive rate vs False positive rate, which of the following are true?
a) The curve is always concave (negative convex).
b) The curve is never concave.
c) The curve may or may not be concave

5)What are the quantities in the Receiver Operating Characteristics (ROC) curve along the x and y axes?
a) x - Precision, y - Recall
b) x - True Positive, y - True Negative
c) x - Specificity, y - Sensitivity
d) x - False Positive Rate, y - True Positive Rate

6) In case of limited training data, which technique, bagging or stacking, would be preferred, and why?
a) Bagging, because we can combine as many classifier as we want by training each on a different sample of the training data
b) Bagging, because we use the same classification algorithms on all samples of the training data
c) Stacking, because each classifier is trained on all of the available data
d) Stacking, because we can use different classification algorithms on the training data

7)How does bagging help in improving the classification performance?
(Multiple answers may be correct)
a) If the parameters of the resultant classifiers are fully uncorrelated (independent), then bagging is inefficient.
b) It helps reduce bias
c) If the parameters of the resultant classifiers are fully correlated, then bagging is inefficient.
d) It helps reduce variance

8)Which among the following prevents over-fitting when we perform bagging?
a) The use of sampling with replacement as the sampling technique
b) The use of weak classifiers
c) The use of classification algorithms which are not prone to overfitting
d) The practice of validation performed on every classifier trained

9)Which of the following statements are TRUE when comparing Committee Machines and Stacking?
(Multiple answers may be correct)
a) Committee Machines are, in general, special cases of 2-layer stacking where the second-layer classifier provides uniform weightage.
b) Both Committee Machines and Stacking have similar mechanisms, but Stacking uses different classifiers while Committee Machines use similar classifiers.
c) Committee Machines are more powerful than Stacking
d) Committee Machines are less powerful than Stacking

10)Which of the following are true about using 5-fold cross validation with a data set of size n = 100 to select the value of k in the kNN algorithm?
(More than one option may be correct)
a) Will always result in the same k since it does not involve any randomness.
b) Might give different answers depending on the splitting in 5 fold cross validation.
c) Does not make sense since n is larger than the number of folds.

Answer 1

1)A,C
Force or ceiling effects and Sample bias

Answer 2

2) A,C & D
a)Precision = TPTP+FP

c)Accuracy = TP+TNTP+TN+FP+FN

d)Recall = TPTP+FN