Machine Learning and its Applications

Quiz
•
Computers
•
University
•
Hard
Dr. Raikwar
Used 61+ times
FREE Resource
15 questions
Show all answers
1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
You are given reviews of movies marked as positive, negative, and neutral. Classifying reviews of a new movie is an example of
Supervised Learning
Unsupervised Learning
Reinforcement Learning
None of these
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
The selling price of a house depends on many factors. For example, it depends on
the number of bedrooms, number of kitchen, number of bathrooms, the year the house was
built, and the square footage of the lot. Given these factors, predicting the selling price of
the house is an example of ____________ task.
Binary Classification
Multilabel Classification
Simple Linear Regression
Multiple Linear Regression
3.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model.)
(i). Models which overfit are more likely to have high bias
(ii). Models which overfit are more likely to have low bias
(iii). Models which overfit are more likely to have high variance
(iv). Models which overfit are more likely to have low variance
(i) and (ii)
(ii) and (iii)
(iii) and (iv)
None of these
4.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
State whether the statements are True or False.
Statement A: When the hypothesis space is richer, overfitting is more likely.
Statement B: When the feature space is larger, overfitting is more likely.
False, False
True, False
True, True
False, True
5.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
What is the purpose of restricting hypothesis space in machine learning?
Can be easier to search
May avoid overfit since they are usually simpler (e.g. linear or low order decision surface)
Both of the above
None of the above
6.
MULTIPLE SELECT QUESTION
1 min • 1 pt
Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?
You will add more features
You will start introducing higher degree features
You will remove some features
None of the above.
7.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
Consider a simple linear regression model with one independent variable (X). The output variable is Y. The equation is : Y=aX+b, where a is the slope and b is the intercept. If we change the input variable (X) by 1 unit, by how much output variable (Y) will change?
1 unit
By slope
By intercept
None
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