Hello friends in this article we will discuss about application of machine learning multiple choice questions and answers.
1) Automatic speech recognition find a wide variety of applications in the ………. domains
A. Medical Assistance
B. Industrial Robotics
C. Defence and Aviation
D. All of the above
Ans : D
2) Electronic Billing is the category of ……..
A. Healthcare
B. Education
C. Legal Sector
D. Energy
Ans. C
3) ………. is most significant application is the analysis of skin image that aim to identify skin cancer.
A. Personalized Medicine
B. Accelerating medical research
C. Medical imaging diagnostics
D. Tools for risk identification
Ans : C
4) Transportation allows communication and …..….. between the two parties
A. Record Keeping
B. Trade
C. Producting
D. Insurance
Ans : B
5) Application of machine learning is ……….
A. Email Filtering
B. Sentimental Analysis
C. Face Recognition
D. All of the above
Ans : D
6) Machine learning is an application of …………
A. Blockchain
B. Artificial Intelligence
C. Both A and B
D. None of the above
Ans : B
7) ……….. is the machine learning algorithms that can be used with labelled data
A. Regression Algorithms
B. Clustering Algorithms
C. Association Algorithms
D. All of the above
Ans : A
8) ………. is the machine learning algorithms that can be used with unlabelled data
A. Regression Algorithms
B.Clustering Algorithms
C. Association Algorithms
D. All of the above
Ans : B
9) Which among the following algorithms are used in Machine learning?
A. Naive Bayes
B. Support Vector Machines
C. K-Nearest Neighbors
D. All of the above
Ans : D
10) ………. is a disadvantage of decision trees?
A. Decision trees are robust to outliers
B. Decision trees are prone to be overfit
C. Both A and B
D.None of the above
Ans : B
11) The supervised learning problems can be grouped as ………..
A. Regression Problems
B. Classification Problems
C. Both A and B
D. None of the above
Ans : C
12) The unsupervised learning problems can be grouped as ……….
A. Clustering
B. Association
C. Both A and B
D. None of the above
Ans : C
13) PCA stands for ………..
A. Principle Components Analysis
B. Principal Components Analysis
C. Principle Communication Analytics
D. Principal Communication Analytics
Ans : B
14) SVM stands for ……….
A. Support Vector Machine
B. Supervised Vector Machine
C. Supervised Variable Machine
D. Support Variable Machine
Ans : A
15) KNN means ……….
A. K Nearest Neighbor
B. K Neighbor Nearest
C. K Nearest Nearest
D. K Neighbor Neighbor
Ans : A
16) A machine learning is an application of ……….
A. Cloud Computing
B. Electronics and Robotics
C. Computer Science
D. Artificial Intelligence
Ans : D
17) A multiple regression model has
A. Only one independent variable
B. More than one dependent variable
C. More than one independent variable
D. None of the above
Ans : B
18) Simple regression assumes a ………. relationship between the input attribute and output attribute
A. Linear
B. Quadratic
C. Reciprocal
D. Inverse
Ans : A
19) Features of Machine Learning are ……….
A. Automation
B. Improved Customer Experience
C. Business Intelligence
D. All of the above
Ans : D
20) Which machine learning models are trained to make a series of decisions based on the rewards and feedback they receive for their actions?
A. Supervised Learning
B. Unsupervised Learning
C. Reinforcement Learning
D. All of the above
Ans : C
21) Which Language is Best for Machine Learning?
A. C
B. Java
C. Python
D. HTML
Ans : C
22) What kind of learning algorithm for “Facial identities or facial expressions”?
A. Prediction
B. Recognition Patterns
C. Generating Patterns
D. Recognizing Anomalies
Ans : B
23) In general, to have a well-defined learning problem, we must identity which of the following
A. The class of tasks
B. The measure of performance to be improved
C. The source of experience
D. All of the above
Ans : D
24) When performing regression or classification, which of the following is the correct way to preprocess the data?
A. Normalize the data -> PCA -> training
B. PCA -> normalize PCA output -> training
C. Normalize the data -> PCA -> normalize PCA output -> training
D. None of the above
Ans : A
25) Classification ……….. the data and Regression ………. the data
A. Separates, Fits
B. Separates, Generates
C. Generates, Fits
D. None of the above
Ans : A