INTRODUCTION – Workflow for Building Complex Models

PRACTICE QUIZ: 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.

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

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

PRACTICE QUIZ: TEST YOUR KNOWLEDGE: PACE IN MACHINE LEARNING: THE PLAN AND ANALYZE STAGES

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

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

3. 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?

QUIZ: MODULE 2 CHALLENGE

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

2. A data professional resolves a class imbalance in a very large dataset. They alter the majority class by using fewer of the original data points in order to produce a split that is more even. What does this scenario describe?

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

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

5. 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.

6. A data professional evaluates a model’s performance and considers how it can be improved. Which PACE stage does this scenario describe?

7. In the model-development process, which type of feature is useful by itself because it contains information that will be useful when forecasting the target?

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

9. A data professional discovers that the dataset they are working with contains a class imbalance. The majority class comprises 90% of the data and the minority class comprises 10% of the data. Which of the following statements best describe the impact of this class imbalance?

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

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

12. 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.

13. 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?

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

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

16. Fill in the blank: Log normalization involves taking the log of a _____ feature and making the data more effective for modeling.

17. Fill in the blank: Log normalization involves reducing _____ in order to make and making the data more effective for modeling.

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

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

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

21. A data professional works with a dataset for a project with their company’s human resources team. They discover that the dataset has a predictor variable that contains more instances of one outcome than another. What will occur as a result of this scenario?

22. A data professional examines a dataset to reveal key details about the data that will help inform the plans for building a model. Which PACE stage does this scenario describe?

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

24. Fill in the blank: Customer _____ is the business term that describes how many customers stop using a product or service, or stop doing business with a company altogether, and at what rate this occurs.

25. Fill in the blank: Naive Bayes is a supervised classification technique that is based on Bayes’ Theorem, with an assumption of _____ among predictors.

26. In classification techniques, what is the term for the proportion of actual positives that are identified correctly to all actual positives?

CONCLUSION – Workflow for Building Complex Models