the nuts and bolts of machine learning coursera week 2 quiz answers

Test your knowledge: PACE in machine learning: The plan and analyze stages

1. Fill in the blank: Feature engineering enables data professionals to take _____ and extract features from it.

  • delimited text
  • a dynamic dashboard
  • a code chunk
  • raw data

2. What term describes the process of modifying existing features in a way that improves accuracy when training a model?

  • Feature selection
  • Feature extraction
  • Feature improvement
  • Feature transformation

3. A class imbalance occurs when a dataset has a predictor variable that contains an equal number of instances of all possible outcomes.

  • True
  • False

Test your knowledge: PACE in machine learning: The construct and execute stages

4. Fill in the blank: Posterior probability is the probability of an event occurring after considering _____ information.

  • historical
  • undefined
  • conditional
  • new

5. A data professional would use the function MinMaxScaler to normalize the columns in a model so that each value falls between zero and one.

  • True
  • False

6. A data professional has built a model, and now they are adjusting how features are engineered in order to improve performance. Which PACE stage does this scenario describe?

  • Plan
  • Execute
  • Analyze
  • Construct

Weekly challenge 2

7. Which of the following statements accurately describe the general categories of feature engineering? Select all that apply.

  • Feature transformation involves modifying existing features in a way that improves accuracy when training a model.
  • Feature extraction involves choosing the features in the data that contribute the most to predicting the response variable.
  • The three general categories of feature engineering are selection, extraction, and transformation.
  • Feature selection involves taking multiple features to create a new one that will improve the accuracy of the algorithm.

8. Which of the following datasets contains a class imbalance that will likely create a problem during analysis?

  • A dataset whose majority class comprises 70% of the data and minority class comprises 30% of the data
  • A dataset whose majority class comprises 90% of the data and minority class comprises 10% of the data
  • A dataset whose classes are split equally, each comprising 50% of the data
  • A dataset whose majority class comprises 60% of the data and minority class comprises 40% of the data

9. Fill in the blank: Customer churn is a business term that describes how many customers stop _____ and at what rate this occurs.

  • doing business with a company
  • writing positive reviews about a company
  • returning items to a company
  • contacting a company’s customer relations department

10. Naive Bayes’s theorem enables data professionals to calculate posterior probability for a data project. What does posterior probability describe?

  • The likelihood of an event occurring after taking into consideration only the most suitable observations and information
  • The likelihood of an event occurring after taking into consideration all new, relevant observations and information
  • The likelihood of an event occurring based upon the observations and information that were available at the start of the data project
  • The likelihood of an event occurring based upon only observations and information that align with current hypotheses

11. Fill in the blank: When normalizing the columns in a dataset using MinMaxScaler, the columns’ maximum value scales to one, and the minimum value scales to _____. Everything else falls somewhere in between.

  • 0.1
  • .5
  • -1
  • 0

12. A data professional is assessing the business need in order to determine what type of model is best suited to a project. Which PACE stage does this scenario describe?

  • Execute
  • Construct
  • Analyze
  • Plan

13. In the model-development process, which type of feature does not contain any useful information for predicting the target variable?

  • Relevant
  • Predictive
  • Irrelevant
  • Conducive

14. Fill in the blank: Log normalization is useful when working with a model that cannot manage continuous variables with _____ distributions.

  • normal
  • skewed
  • probability
  • binomial

15. What occurs when a dataset has a predictor variable that contains more instances of one outcome than another?

  • Incompatibility
  • Class imbalance
  • Redundancy
  • Inconsistent data

16. Fill in the blank: Customer churn is the business term that describes how many customers stop _____ and at what rate this occurs.

  • using a product or service
  • sharing feedback with a company
  • researching a company’s offerings
  • reviewing items online

17. Naive Bayes is a supervised classification technique that assumes independence among predictors. What is the meaning of this concept?

  • The value of a predictor variable on a given class is not affected by the values of other predictors.
  • The value of a predictor variable on a given class is equal to the values of other predictors.
  • The value of a predictor variable on a given class is measured by the values of other predictors.
  • The value of a predictor variable on a given class is dependent upon the values of other predictors.

18. Which of the following statements accurately describe feature engineering? Select all that apply.

  • Feature engineering does not involve using a data professional’s statistical knowledge.
  • In feature engineering, feature extraction involves taking multiple features to create a new one that will improve the accuracy of the algorithm.
  • In feature engineering, feature selection involves choosing the features in the data that contribute the most to predicting the response variable.
  • Feature engineering may involve transforming the properties of raw data.

19. What does Bayes’s theorem enable data professionals to calculate?

  • Margin of error
  • Data accuracy
  • Posterior probability
  • Causation

20. Fill in the blank: When using a scaler to _____ the columns in a dataset using MinMaxScaler, a data professional must fit the scaler to the training data and transform both the training data and the test data using that same scaler.

  • filter
  • customize
  • sort
  • normalize

21. A data professional is evaluating a model’s performance and considering how it can be improved. Which PACE stage does this scenario describe?

  • Plan
  • Construct
  • Analyze
  • Execute

22. In the model-development process, which type of feature is not useful by itself for predicting the target variable, but becomes predictive in conjunction with other features?

  • Predictive
  • Interactive
  • Redundant
  • Irrelevant

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