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Machine Learning (Concept Learning)

Total questions: 25

Worksheet time: 17mins

Name
Class
Date
1.

_______ is the subset of objects or events defined over a larger set.

a)

Entropy

b)

Hierarchy

c)

Concept

d)

Learning

2.

In hypothesis representation, what is the meaning of the symbol ' ? '

a)

No value is acceptable

b)

Any value is acceptable

c)

Selected values are acceptable

d)

None of the above

3.

Positive examples of the data set are called as _________

a)

hypothesis

b)

Target concept

c)

non members of the concept

d)

members of the concept

4.

The goal of concept learning is to find __________

a)

hypothesis

b)

training data set

c)

testing data set

d)

concept

5.

Data set which has d binary features contains ______ concepts

a)


2d2^d

b)

22d2^{2^d}

c)

4d4^d

d)

42d4^{2^d}

6.

Which statements is/are true related to Find - s algorithm

a)

considers both positive and negative samples

b)

considers only positive samples

c)

includes version space

d)

No way to determine whether the hypothesis is consistent or not.

7.

In Find-s algorithm, if the attribute value is matched with hypothesis value, what action has to be done?

a)

No action. keep the value as it is.

b)

replace it by ?

c)

replace it by $

d)

re phrase the entire hypothesis

8.

________ is the simplest supervised machine learning algorithm used for classification based on how its neighbors are classified.

a)

ANN

b)

KNN

c)

SVM

d)

Clustering

9.

How to choose the value of K in KNN?

a)

Take the square root of the total data point available in the dataset.

b)

Take the mean of the total data point available in the dataset.

c)

Take the variance of the total data point available in the dataset.

d)

Take the standard deviation of the total data point available in the dataset.

10.

______ is the process of dividing datasets into different categories or groups by adding labels.

a)

Regression

b)

Clustering

c)

Classification

d)

K-means

11.

______ is the graphical representation of all possible solutions to a decision.

a)

K-Means

b)

KNN

c)

ANN

d)

Decision Tree

12.

______ is the classification Technique based on Baye's Theorem.

a)

Decision Tree

b)

SVM

c)

Naive Bayes

d)

Association

13.

In Decision Tree, _____ is dividing the root node into different parts on the basis of some condition.

a)

pruning

b)

splitting

c)

leaf node

d)

parent node

14.

_____ is a metrics used to measure impurity or randomness in data.

a)

Sigmoid

b)

Entropy

c)

Step function

d)

Categorical data

15.

______ is a classification algorithm used to assign observations to a discrete set of classes.

a)

Linear Regression

b)

Multiple Linear Regression

c)

Logistic Regression

d)

Classification

16.

The classification of spam mail and normal mail falls under _________ category.

a)

Binary Logistic Regression

b)

Multinomial Logistic Regression

c)

Ordinal Logistic Regression

d)

Linear Regression

17.

____ is the fundamental unit of brain.

a)

Lypcoses

b)

Andrite

c)

Neuron

d)

Axon

18.

In SVM, non linear problem can be solved by transforming data from _____ dimensional space into _____ dimensional space.

a)

high, low

b)

low, high

c)

low, medium

d)

medium, high

19.

______ techniques combine individual models together to improve the stability and predictive power of the model.

a)

SVM

b)

ANN

c)

Ensemble

d)

KNN

20.

Random Forest concepts are used to overcome ______

a)

over fitting

b)

complications in data

c)

complexity in model

d)

data noise

21.

______ is a modeling error that occurs when a function is too closely fit to a limited set of data points.

a)

under fitting

b)

over fitting

c)

linear fitting

d)

logistic fitting

22.

______ is the partition approach of grouping similar datas.

a)

KNN

b)

K-Means

c)

ANN

d)

HAC

23.

The prior goal of unsupervised learning model is to determine the ________ .

a)

classification task

b)

regression task

c)

accuracy of the classified labels

d)

data patterns

24.

Agglomerative clustering follows ________ .

a)

top down approach.

b)

bottom up approach

c)

partitional clustering

d)

model based clustering

25.

Single linkage is the process of finding the distance between the _______ members of the two clusters.

a)

farest

b)

closest

c)

average

d)

mean