#
*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*

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__Machine
learning MCQ - Set 04__

__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 variacceOnce you have more training examples you’ll have
lower test-error (variance of the model decrease, meaning we are less
overfitting).Refer here for more details: In Machine Learning, What is Better: More Data or better Algorithms##
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) MaximizationWe 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 inferenceM-step: apply
standard MLE/MAP method to completed dataRefer here for
more: Relationship between EM, MLE and MAP |

**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 lineSVM 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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