Friday, May 8, 2020

Machine Learning Multiple Choice Questions and Answers 07

Top 5 Machine Learning Quiz Questions with Answers explanation, Interview questions on machine learning, quiz questions for data scientist answers explained, machine learning exam questions

Machine learning MCQ - Set 07

1. Which of the following can only be used when training data are linearly separable?

a) Linear hard-margin SVM.
b) Linear Logistic Regression.
c) Linear Soft margin SVM.
d) The centroid method.

View Answer

Answer: (a) Linear hard-margin SVM
Hard margin SVM can work only when data is completely linearly separable without any errors (noise or outliers). This is called as hard margin SVM since we have very strict constraints to correctly classify each and every data points.

2. Consider the Bayesian network given below. How many independent parameters would we need if we made no assumptions about independence or conditional independence?

a) 3
b) 4
c) 7
d) 15

View Answer

Answer: (d) 15
A model which makes no conditional independence assumptions would need 24−1 = 15 parameters.
Parameter estimation:
A straightforward representation of the join probability distribution over n binary variables requires us to represent the probability of every combination of states of these variables. For n binary variables, for example, we have 2n-1 such combinations.

3. The K-means algorithm:

a) Requires the dimension of the feature space to be no bigger than the number of samples
b) Has the smallest value of the objective function when K = 1
c) Minimizes the within class variance for a given number of clusters
d) Converges to the global optimum if and only if the initial means are chosen as some of the samples themselves

View Answer

Answer: (c) Minimizes the within class variance for a given number of clusters

The objective of K-Means clustering is to minimize total intra-cluster variance.

Within-cluster-variance is a simple to understand measure of compactness (compact partitioning).
K-means minimizes intra-cluster variance (tighter clusters); that is, the discovered clusters minimize the sum of the squared distances between data points and the center (centroid) of their containing cluster.

4. Which one of the following is equal to P(A, B, C) given Boolean random variables A, B and C, and no independence or conditional independence assumptions between any of them?

a) P(A | B) * P(B | C) * P(C | A)
b) P(C | A, B) * P(A) * P(B)
c) P(A, B | C) * P(C)
d) P(A | B, C) * P(B | A, C) * P(C | A, B)

View Answer

Answer: (c) P(A, B | C) * P(C)
P(A, B, C) = P(A, B | C) * P(C).

5. For polynomial regression, which one of these structural assumptions is the one that most affects the trade-off between underfitting and overfitting:

a) The polynomial degree
b) Whether we learn the weights by matrix inversion or gradient descent
c) The assumed variance of the Gaussian noise
d) The use of a constant-term unit input

View Answer

Answer: (a) the polynomial degree
Choosing the right degree of polynomial plays a critical role in fit of regression. Higher-order polynomials can be a serious abuse of regression analysis. If we choose higher degree of polynomial, chances of overfit increase significantly. And the model with higher degree of polynomial will fail to generalize on unseen data.
A high degree polynomial closely fits more number of points, hence the bias is low. While a low degree polynomial does not have this expressivity leading to high bias.


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