data science methodology coursera week 2 quiz answers
From Understanding to Preparation
1. In the case study, working through the Data Preparation stage, it was revealed that the initial definition was not capturing all of the congestive heart failure admissions that were expected, based on clinical experience.
- True
- False
2. Select the correct statement about what data scientists do during the Data Preparation stage.
- During the Data Preparation stage, data scientists define the variables to be used in the model.
- During the Data Preparation stage, data scientists determine the timing of events.
- During the Data Preparation stage, data scientists aggregate the data and merge them from different sources.
- During the Data Preparation stage, data scientists identify missing data.
- All of the above statements are correct.
3. The Data Preparation stage is a very iterative and complicated stage that cannot be accelerated through automation.
- True
- False
From Modeling to Evaluation
5. Model Evaluation includes ensuring that the data are properly handled and interpreted.
- True
- False
6. Select the correct statements about the ROC curve.
- The ROC curve is a useful diagnostic tool for determining the optimal classification model.
- The ROC curve was originally developed to optimize healthcare and detect congestive heart failure readmission rate.
- By plotting the true-positive rate against the false-positive rate for different values of the relative misclassification cost, the ROC curve can be used to select the optimal model.
- ROC stands for Receiver Operating Characteristic curve, which was originally developed to detect enemy aircrafts on radar.
