Hello friends in this post we are going to share a simple Machine Learning Application Multiple Choice Questions for your examination in college or university which will be really become helpful to you.
1) What is true about Machine Learning?
A. Machine Learning (ML) is that field of computer science
B. ML is a type of artificial intelligence that extract patterns out of raw data by using an
algorithm or method
C. The main focus of ML is to allow computer systems learn from experience without being
explicitly programmed or human intervention
D. All of the above
Ans : D
2) Which of the following is ML real world application?
A. Digital Assistance
B. Image Recognition
C. Fraud Detection
D. All of the above
Ans: D
3) Which of the following is application of ML in Pharma & Medicine?
A. Clinical trial research
B. Smart electronic health record
C. Disease identification
D. All of the above
Ans : D
4) Which are the applications in finance?
A. Security
B. Financial Monitoring
C. Risk Management
D. All of above
Ans. D
5) Clinical trial research is the application of _______
A. Healthcare
B. Legal Sector
C. Pharmaceuticals
D. Energy
Ans : C
6) Document Automation is the category of _____
A. Healthcare
B. Legal Sector
C. Education
D. Energy
Ans : B
7) Which of the following are types of Machine Learning?
A. Supervised Learning
B. Unsupervised Learning
C. Reinforcement Learning
D. All of the above
Ans : D
8) Which of the following is not a supervised learning?
A. PCA
B. Naive Bayesian
C. Linear Regression
D. Decision Tree
Ans: . A
9) Machine Learning is a field of AI consisting of learning algorithms that ______
A. At executing some task
B. Over time with experience
C. Improve their performance
D. All of the above
Ans :. D
10) Which of the following statement is False in the case of the KNN Algorithm?
C. KNN is used only for classification problem statements
Ans : C
11) The robotic arm will be able to paint every corner in the automotive parts while minimizing
C. Reinforcement Learning
Ans: C
12) How do you choose the right node while constructing a decision tree?
A. An attribute having high entropy
B. An attribute having high entropy and information gain
C. An attribute having the lowest information gain
D. An attribute having the highest information gain
Ans : D
13) What is classification?
A. When the output variable is a category, such as “red” or “blue” or “disease” and “no disease”
B. When the output variable is a real value, such as “dollars” or “weight”
C. Both A and B
D. None of these
Ans: A
14) What is regression?
A. When the output variable is a category, such as “red” or “blue” or “disease” and “no disease”
B. When the output variable is a real value, such as “dollars” or “weight”
C. Both A and B
D. None of these
Ans : B
15) Supervised learning and unsupervised clustering both require at least one
16) A nearest neighbor approach is best used
17) Classification problems are distinguished from estimation problems in that
Ans. C
18) The supervised learning technique can process both numeric and categorical input attributes
A. Linear Regression
B. Bayes Classifier
C. Logistic Regression
D. Backpropagation Learning
Ans : A
19) ………. clustering algorithm merges and splits nodes to help modify nonoptimal partitions
A. Agglomerative Clustering
B. Expectation Maximization
C. Conceptual Clustering
D. K-Means Clustering
Ans : D