Advanced Database Management System - Tutorials and Notes: Machine Learning Multiple Choice Questions and Answers 04

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Friday, 8 May 2020

Machine Learning Multiple Choice Questions and Answers 04

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 04

1. As the number of training examples goes to infinity, your model trained on that data will have:
a) Lower variance
b) Higher variance
c) Same variance
d) None of the above

View Answer

Answer: (a) Lower variacce
Once you have more training examples you’ll have lower test-error (variance of the model decrease, meaning we are less overfitting).

High-variance – a model that represent training set well, but at risk of overfitting to noisy or unrepresentative training data.

High bias – a simpler model that doesn’t tend to overfit, but may underfit training data, failing to capture important regularities.

2. Suppose we like to calculate P(H|E, F) and we have no conditional independence information. Which of the following sets of numbers are sufficient for the calculation?

a) P(E, F), P(H), P(E|H), P(F|H)
b) P(E, F), P(H), P(E, F|H)
c) P(H), P(E|H), P(F|H)
d) P(E, F), P(E|H), P(F|H)

View Answer

Answer: (b) P(E, F), P(H), P(E, F|H)
This is Bayes’ rule;
P(H|E F) = (P(E F|H)*P(H)) / P(E F)

3. Suppose you are given an EM algorithm that finds maximum likelihood estimates for a model with latent variables. You are asked to modify the algorithm so that it finds MAP estimates instead. Which step or steps do you need to modify?

a) Expectation
b) Maximization
c) No modification necessary
d) Both

View Answer

Answer: (b) Maximization
We need to modify Maximization step.
EM is an optimization strategy for objective functions that can be interpreted as likelihoods in the presence of missing data. EM is an iterative algorithm with two linked steps:
E-step: fill-in hidden values using inference
M-step: apply standard MLE/MAP method to completed data

4. Which of the following is/are true regarding an SVM?
a) For two dimensional data points, the separating hyperplane learnt by a linear SVM will be a straight line.
b) In theory, a Gaussian kernel SVM cannot model any complex separating hyperplane.
c) For every kernel function used in a SVM, one can obtain an equivalent closed form basis expansion.
d) Overfitting in an SVM is not a function of number of support vectors.

View Answer

Answer: (a) For two dimensional data points, the separating hyperplane learnt by a linear SVM will be a straight line
SVM or Support Vector Machine is a linear model for classification and regression problems. It can solve linear and non-linear problems and work well for many practical problems. The algorithm creates a line or a hyperplane which separates the data into classes.

A hyperplane in an n-dimensional Euclidean space is a flat, n-1 dimensional subset of that space that divides the space into two disconnected parts.

5. Which of the following best describes what discriminative approaches try to model? (w are the parameters in the model)

a) p(y|x, w)
b) p(y, x)
c) p(w|x, w)
d) None of the above

View Answer

Answer: (a) p(y|x, w)
Machine learning is to learn a (random) function that maps a variable X (feature) to a variable Y(class) using a (labeled) dataset.
A Generative Model learns the joint probability distribution p(x,y). It predicts the conditional probability with the help of Bayes Theorem. To get the conditional probability P(Y|X), generative models estimate the prior P(Y) and likelihood P(X|Y) from training data and use Bayes rule to calculate the posterior P(Y |X)

Discriminative approaches model the posterior probability P(y|x) directly.


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