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Hello Learners, Today, we are going to share Machine Learning – Dimensionality Reduction Cognitive Class Course Exam Answer launched by IBM. This certification course is totally free of cost✅✅✅ for you and available on Cognitive Class platform.
Here, you will find Machine Learning – Dimensionality Reduction Exam Answers in Bold Color which are given below.
These answers are updated recently and are 100% correct✅ answers of all modules and final exam answers of Machine Learning – Dimensionality Reduction from Cognitive Class Certification Course.
| Course Name | Machine Learning – Dimensionality Reduction |
| Organization | IBM |
| Skill | Online Education |
| Level | Beginner |
| Language | English |
| Price | Free |
| Certificate | Yes |
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Machine Learning – Dimensionality Reduction Cognitive Class Course Exam Answer
Module 1: Data Series
Question 1: Which of the following techniques can be used to reduce the dimensions of the population?
- Exploratory Data Analysis
- Principal Component Analysis
- Exploratory Factor Analysis
- Cluster Analysis
Question 2: Cluster Analysis partitions the columns of the data, whereas principal component and exploratory factor analyses partition the rows of the data. True or false?
- False
- True
Question 3: Which of the following options are true? Select all that apply.
- PCA explains the total variance
- EFA explains the common variance
- EFA identifies measures that are sufficiently similar to each other to justify combination
- PCA captures latent constructs that are assumed to cause variance
Module 2: Data Refinement
Question 1: Which of the following options is true?
- A matrix of correlations describes all possible pairwise relationships
- Eigenvalues are the principal components
- Correlation does not explain the covariation between two vectors
- Eigenvectors are a measure of total variance, as explained by the principal components
Question 2: PCA is a method to reduce your data to the fewest ‘principal components’ while maximizing the variance explained. True or false?
- False
- True
Question 3: Which of the following techniques was NOT covered in this lesson?
- Parallel analysis
- Percentage of Common Variance
- Scree Test
- Kaiser-Guttman Rule
Module 3: Exploring Data
Question 1: EFA is commonly used in which of the following applications? Select all that apply.
- Customer satisfaction surveys
- Personality tests
- Performance evaluations
- Image analysis
Question 2: Which of the following options is an example of an Oblique Rotation?
- Regmax
- Varimax
- Softmax
- Promax
Question 3: An Orthogonal Rotation assumes that factors are correlated with each other. True or false?
- False
- True
Final Exam Answers – Machine Learning – Dimensionality Reduction Cognitive Class
Question 1: Why might you use cluster analysis as an analytic strategy?
- To identify higher-order dimensions
- To identify outliers
- To reduce the number of variables
- To segment the market
- None of the above
Question 2: Suppose you have 100,000 individuals in a dataset, and each individual varies along 60 dimensions. On average, the dimensions are correlated at r = .45. You want to group the variables together, so you decide to run principle component analysis. How many meaningful, higher-order components can you extract?
- 60
- 3
- 20
- 24
- The answer cannot be determined
Question 3: What technique should you use to identify the dimensions that hang together?
- Principal axis factoring
- Confirmatory factor analysis
- Exploratory factor analysis
- Two of the above
- None of the above
Question 4: What are loadings?
- Covariance between the two factors
- Correlations between each variable and its factor
- Correlations between each variable and its component
- Two of the above
- None of the above
Question 5: When would you use PCA over EFA?
- When you want to use an orthogonal rotation
- When you are interested in explaining the total variance in a variance-covariance matrix
- When you have too many variables
- When you are interested in a latent construct
- None of the above
Question 6: What is uniqueness?
- A measure of replicability of the factor
- The amount of variance not explained by the factor structure
- The amount of variance explained by the factor structure
- The amount of variance explained by the factor
- None of the above
Question 7: Suppose you are looking to extract the major dimensions of a parrot’s personality. Which technique would you use?
- Maximum likelihood
- Principal component analysis
- Cluster analysis
- Factor analysis
- None of the above
Question 8: Suppose you have 60 variables in a dataset, and you know that 2 components explain the data very well. How many components can you extract?
- 45
- 5
- 60
- 2
- None of the above
Question 9: When would you use an orthogonal rotation?
- When correlations between the variables are large
- When you observe small correlations between the variables in the dataset
- When you think that the factors are uncorrelated
- All of the above
- None of the above
Question 10: When would you use confirmatory factor analysis?
- When you want to validate the factor solution
- When you want to explain the variance in the matrix accounting for the measurement error
- When you want to identify the factors
- Two of the above
- None of the above
Question 11: Which of the following is NOT a rule when deciding on the number of factors?
- Newman-Frank Test
- Percentage of common variance explained
- Scree test
- Kaiser-Guttman
- None of the above
Question 12: What is one assumption of factor analysis?
- A number of factors can be determined via the Scree test
- Factor analysis will extract only unique factors
- A latent variable causes the variance in observed variables
- There is no measurement error
- None of the above
Question 13: What is an eigenvector?
- The proportion of the variance explained in the matrix
- A higher-order dimension that subsumes all of the lower-order errors
- A higher-order dimension that subsumes similar lower-order dimensions
- A higher-order dimension that subsumes all lower-order dimensions
- None of the above
Question 14: What is a promax rotation?
- A rotation method that minimizes the square loadings on each factor
- A rotation method that maximizes the variance explained
- A rotation method that maximizes the square loadings on each factor
- A rotation method that minimizes the variance explained
- None of the above
Question 15: What is the cut-off point for the Common Variance Explained rule?
- 80% of variance explained
- 50% of variance explained
- 3 variables
- 1 unit
- None of the above
Question 16: Why would you try to reduce dimensions?
- Individuals need to be placed into groups
- Variables are highly-correlated
- Many variables are likely assessing the same thing
- Two of the above
- All of the above
Question 17: If you have 20 variables in a dataset, how many dimensions are there?
- At most 20
- At least 20
- As many as the number of factors you can extract
- Not enough information
- None of the above
Question 18: What term describes the amount of variance of each variable explained by the factor structure?
- Eigenvector
- Commonality
- Similarity
- Communality
- None of the above
Question 19: What package contains the necessary functions to perform PCA and EFA?
- ggplot2
- FA
- psych
- factAnalis
- None of the above
Question 20: What is the best method for identifying the number of factors to extract?
- Parallel Analysis
- Scree test
- Newman-Frank Test
- Percentage of common variance explained
- All of the above
Conclusion
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