Machine Learning Set 27 MCQs

Q1 | Which of the following is true about Residuals ?

A) Lower is better

B) Higher is better

C) A or B depend on the situation

D) None of these

Q2 | Which of the following statement is true about outliers in Linear regression?

A) Linear regression is sensitive to outliers

B) Linear regression is not sensitive to outliers

C) Can’t say

D) None of these

Q3 | Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?

A) Since the there is a relationship means our model is not good

B) Since the there is a relationship means our model is good

C) Can’t say

D) None of these

Q4 | Naive Bayes classifiers are a collection ------------------of algorithms

Classification

Clustering

Regression

All

Q5 | Naive Bayes classifiers is _______________ Learning

Supervised

Unsupervised

Both

None

Q6 | Features being classified is independent of each other in Naïve Bayes Classifier

False

true

Q7 | Features being classified is __________ of each other in Naïve Bayes Classifier

Independent

Dependent

Partial Dependent

None

Q8 | Conditional probability is a measure of the probability of an event given that another event has already occurred.

True

false

Q9 | Bayes’ theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

True

false

Q10 | Bernoulli Naïve Bayes Classifier is ___________distribution

Continuous

Discrete

Binary

Q11 | Multinomial Naïve Bayes Classifier is ___________distribution

Continuous

Discrete

Binary

Q12 | Gaussian Naïve Bayes Classifier is ___________distribution

Continuous

Discrete

Binary

Q13 | Binarize parameter in BernoulliNB scikit sets threshold for binarizing of sample features.

True

false

Q14 | Gaussian distribution when plotted, gives a bell shaped curve which is symmetric about the _______ of the feature values.

Mean

Variance

Discrete

Random

Q15 | SVMs directly give us the posterior probabilities P(y = 1jx) and P(y = 􀀀1jx)

True

false

Q16 | Any linear combination of the components of a multivariate Gaussian is a univariate Gaussian.

True

false

Q17 | Solving a non linear separation problem with a hard margin Kernelized SVM (Gaussian RBF Kernel) might lead to overfitting

True

false

Q18 | SVM is a ------------------ algorithm

Classification

Clustering

Regression

All

Q19 | SVM is a ------------------ learning

Supervised

Unsupervised

Both

None

Q20 | The linear SVM classifier works by drawing a straight line between two classes

True

false

Q21 | What is Model Selection in Machine Learning?

The process of selecting models among different mathematical models, which are used to describe the same data set

when a statistical model describes random error or noise instead of underlying relationship

Find interesting directions in data and find novel observations/ database cleaning

All above

Q22 | Which are two techniques of Machine Learning ?

Genetic Programming andInductive Learning

Speech recognition and Regression

Both A & B

None of the Mentioned

Q23 | Even if there are no actual supervisors ________ learning is also based on feedback provided by the environment

Supervised

Reinforcement

Unsupervised

None of the above

Q24 | When it is necessary to allow the model to develop a generalization ability and avoid a common problem called______.

Overfitting

Overlearning

Classification

Regression

Q25 | Techniques involve the usage of both labeled and unlabeled data is called___.

Supervised

Semi-supervised

Unsupervised

None of the above