Machine Learning Set 2 MCQs

Q1 | Like the probabilistic view, the ________ view allows us to associate a probability of membership with each classification.

exampler

deductive

classical

inductive

Q2 | Database query is used to uncover this type of knowledge.

deep

hidden

shallow

multidimensional

Q3 | Some telecommunication company wants to segment their customers into distinct groups ,this is an example of

supervised learning

reinforcement learning

unsupervised learning

data extraction

Q4 | In the example of predicting number of babies based on stork's population ,Number of babies is

outcome

feature

observation

attribute

Q5 | Which learning Requires Self Assessment to identify patterns within data?

unsupervised learning

supervised learning

semisupervised learning

reinforced learning

Q6 | Select the correct answers for following statements.1. Filter methods are much faster compared to wrapper methods.2. Wrapper methods use statistical methods for evaluation of a subset of features while Filter methods use cross validation.

both are true

1 is true and 2 is false

both are false

1 is false and 2 is true

Q7 | The "curse of dimensionality" referes

all the problems that arise when working with data in the higher dimensions, that did not exist in the lower dimensions.

all the problems that arise when working with data in the lower dimensions, that did not exist in the higher dimensions.

all the problems that arise when working with data in the lower dimensions, that did not exist in the lower dimensions.

all the problems that arise when working with data in the higher dimensions, that did not exist in the higher dimensions.

Q8 | In simple term, machine learning is

training based on historical data

prediction to answer a query

both a and b??

automization of complex tasks

Q9 | If machine learning model output doesnot involves target variable then that model is called as

descriptive model

predictive model

reinforcement learning

all of the above

Q10 | Following are the descriptive models

clustering

classification

association rule

both a and c

Q11 | Different learning methods does not include?

memorization

analogy

deduction

introduction

Q12 | A measurable property or parameter of the data-set is

training data

feature

test data

validation data

Q13 | Feature can be used as a

binary split

predictor

both a and b??

none of the above

Q14 | It is not necessary to have a target variable for applying dimensionality reduction algorithms

true

false

Q15 | The most popularly used dimensionality reduction algorithm is Principal Component Analysis (PCA). Which of the following is/are true about PCA? 1. PCA is an unsupervised method2. It searches for the directions that data have the largest variance3. Maximum number of principal components <= number of features4. All principal components are orthogonal to each other

1 & 2

2 & 3

3 & 4

all of the above

Q16 | Which of the following is a reasonable way to select the number of principal components "k"?

choose k to be the smallest value so that at least 99% of the varinace is retained. - answer

choose k to be 99% of m (k = 0.99*m, rounded to the nearest integer).

choose k to be the largest value so that 99% of the variance is retained.

use the elbow method

Q17 | Which of the folllowing is an example of feature extraction?

construction bag of words from an email

applying pca to project high dimensional data

removing stop words

forward selection

Q18 | Prediction is

the result of application of specific theory or rule in a specific case

discipline in statistics used to find projections in multidimensional data

value entered in database by expert

independent of data

Q19 | PCA works better if there is1. A linear structure in the data2. If the data lies on a curved surface and not on a flat surface3. If variables are scaled in the same unit

1 and 2

2 and 3

1 and 3

1,2 and 3

Q20 | A student Grade is a variable F1 which takes a value from A,B,C and D. Which of the following is True in the following case?

variable f1 is an example of nominal variable

variable f1 is an example of ordinal variable

it doesn\t belong to any of the mentioned categories

it belongs to both ordinal and nominal category

Q21 | What can be major issue in Leave-One-Out-Cross-Validation(LOOCV)?

low variance

high variance

faster runtime compared to k-fold cross validation

slower runtime compared to normal validation

Q22 | Imagine a Newly-Born starts to learn walking. It will try to find a suitable policy to learn walking after repeated falling and getting up.specify what type of machine learning is best suited?

classification

regression

kmeans algorithm

reinforcement learning

Q23 | Support Vector Machine is

logical model

proababilistic model

geometric model

none of the above