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.
Shuffle Q/A 2
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