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Quiz on Machine Learning

Total questions: 20

Worksheet time: 10mins

Name
Class
Date
1.

What is the main goal of machine learning?

a)

To make computers intelligent

b)

To automate manual tasks

c)

To enable computers to learn from data

d)

To create self-aware machines

2.

Which type of machine learning algorithm aims to mimic the process of human learning?

a)

Supervised learning

b)

Unsupervised learning

c)

Reinforcement learning

d)

Deep learning

3.

Which machine learning algorithm is suitable for solving regression problems?

a)

K-Means clustering

b)

Random Forest

c)

K-Nearest Neighbors (KNN)

d)

Apriori algorithm

4.

Which technique is used to reduce the dimensionality of data while preserving as much information as possible?

a)

Clustering

b)

Feature extraction

c)

Feature selection

d)

Regularization

5.

Which algorithm is used for finding frequent itemsets in transactional databases?

a)

Decision Trees

b)

K-Means clustering

c)

Apriori algorithm

d)

Support Vector Machines (SVM)

6.

Which algorithm is used for hierarchical clustering?

a)

K-Means clustering

b)

Agglomerative clustering

c)

DBSCAN

d)

Principal Component Analysis (PCA)

7.

In a neural network, what are the layers between the input and output layers called?

a)

Hidden layers

b)

Output layers

c)

Feature layers

d)

Input layers

8.

Which machine learning algorithm is inspired by the functioning of the human brain's neural networks?

a)

K-Means clustering

b)

Decision Trees

c)

Support Vector Machines (SVM)

d)

Artificial Neural Networks

9.

In a k-fold cross-validation, how is the dataset divided?

a)

It is divided into \(k\) equal subsets

b)

It is divided into \(k-1\) training subsets and 1 validation subset

c)

It is divided into training and testing subsets based on a ratio

d)

It is divided randomly into disjoint subsets

10.

Which algorithm is used for finding the optimal clustering of data points?

a)

Random Forest

b)

K-Means clustering

c)

Support Vector Machines (SVM)

d)

Hierarchical clustering

11.

Which machine learning approach is based on the assumption that similar data points are more likely to have the same labels?

a)

Clustering

b)

Classification

c)

Regression

d)

Anomaly detection

12.

Which of the following machine learning algorithm is based upon the idea of bagging?

a)

Decision tree

b)

Random-forest

c)

Classification

d)

Regression

13.

Choose the real-world application of ML from the following.

a)
  1. Fraud detection

b)
  1. Chatbots

c)
  1. Digital assistants

d)
  1. All of the above

14.


A collection of one or more items is called as

a)


Itemset

b)

Support

c)


Confidence 

d)

Support Count

15.


An itemset whose support is greater than or equal to a minimum support threshold is ______

a)


Itemset

b)

Frequent Itemset

c)


Infrequent items

d)


Threshold values

16.


How do you calculate Confidence (A -> B)?

a)


Support(A ∩ B) / Support (A)

b)

Support(A ∩ B) / Support (B)

c)

Support(A ∪ B) / Support (A)

d)


Support(A ∪ B) / Support (B)

17.


For the question given below consider the data Transactions :

  1. 1. I1, I2, I3, I4, I5, I6

  2. 2. I7, I2, I3, I4, I5, I6

  3. 3. I1, I8, I4, I5

  4. 4. I1, I9, I10, I4, I6

  5. 5. I10, I2, I4, I11, I5

a)


<I1>, <I2>, <I4>, <I5>, <I6>, <I1, I4>, <I2, I4>, <I2, I5>, <I4, I5>, <I4, I6>, <I2, I4, I5>

b)

<I2>, <I4>, <I5>, <I2, I4>, <I2, I5>, <I4, I5>, <I2, I4, I5>

c)


<I11>, <I4>, <I5>, <I6>, <I1, I4>, <I5, I4>, <I11, I5>, <I4, I6>, <I2, I4, I5>

d)


<I1>, <I4>, <I5>, <I6>

18.

Which of the following is required by K-means clustering?

a)


Defined distance metric

b)


Number of clusters

c)


Initial guess as to cluster centroids

d)


All of the above

19.


In the K-means algorithm for partitioning, each cluster is represented by the ___ of objects in the cluster.

a)

Means

b)

Medians

c)

Modes

d)

Members

20.

A perceptron is:

a)

a single layer feed-forward neural network with pre-processing

b)

an auto-associative neural network

c)

a double layer auto-associative neural network

d)

a neural network that contains feedback