INTRODUCTION – Logistic Regression

PRACTICE QUIZ: TEST YOUR KNOWLEDGE: FOUNDATIONS OF LOGISTIC REGRESSION

1. No extreme outliers is one of the four main binomial logistic regression assumptions. What are the other three? Select all that apply.

2. Logit is the logarithm of the odds of a given probability.

3. Fill in the blank: The maximum likelihood estimation is a technique used for estimating the beta parameters that _____ the likelihood of a model producing the observed data.

PRACTICE QUIZ: TEST YOUR KNOWLEDGE: LOGISTIC REGRESSION WITH PYTHON

1. When building a logistic regression model, what does CLF stand for?

2. Which package do you use to create a plot of your model to visualize its results?

PRACTICE QUIZ: TEST YOUR KNOWLEDGE: INTERPRET LOGISTIC REGRESSION RESULTS

1. The confusion matrix is a graphical representation of how accurate a classifier is at predicting what for a categorical variable?

2. Fill in the blank: _____ measures the proportion of positive predictions that were true positives.

3. Which of the following provide additional information about the likelihood of a result being merely by chance? Select all that apply.

PRACTICE QUIZ: TEST YOUR KNOWLEDGE: COMPARE REGRESSION MODELS

1. Which model might a data professional consider first if the outcome variable is binary?

2. A data professional can use recall to evaluate a logistic regression model. What other metrics can be used to meet this goal? Select all that apply.

QUIZ: MODULE 5 CHALLENGE

1. Fill in the blank: Binomial logistic regression is a technique that models the _____ of an observation falling into one of two categories, based on one or more independent variables.

2. A data professional calculates a logarithm of the odds of a given probability. What are they calculating?

3. Fill in the blank: Maximum likelihood estimation is a technique for estimating the _____ that maximize the likelihood of the model producing the observed data.

4. Following the no extreme outlier assumption, when are outliers detected?

5. What graphical representation demonstrates a classifier’s accuracy at predicting the labels for a categorical variable?

6. A data professional calculates precision in logistic regression results. They have 101 true positives, 63 true negatives, 4 false positives, and 2 false negatives. What is the calculation for precision?

7. A data professional calculates accuracy in logistic regression results. They have 99 true positives, 91 true negatives, and 248 total predictions. What is the calculation for accuracy?

8. A data professional calculates recall in logistic regression results. They have 145 true positives, 128 true negatives, 4 false positives, and 2 false negatives. What is the calculation for recall?

9. What technique models the probability of an observation falling into one of two categories, based on one or more independent variables?

10. What is the logit formula?

11. What technique estimates the beta parameters that increase the likelihood of the model producing observed data?

12. Which regression assumption states that, if multiple X variables are in a model, they should not be highly correlated with one another?

13. Fill in the blank: A confusion matrix is a graphical representation of how accurate a classifier is at _____ the labels for a categorical variable.

14. A data professional calculates precision in logistic regression results. They have 89 true positives, 83 true negatives, 3 false positives, and 1 false negative. What is the calculation for precision?

15. A data professional calculates accuracy in logistic regression results. They have 82 true positives, 75 true negatives, and 202 total predictions. What is the calculation for accuracy?

16. A data professional calculates recall in logistic regression results. They have 91 true positives, 84 true negatives, 6 false positives, and 5 false negatives. What is the calculation for recall?

17. Logit includes which other probability formula?

18. Fill in the blank: A confusion matrix is a graphical representation of how accurate a classifier is at predicting the labels for a _____ variable.

19. Precision measures the proportion of positive predictions that were false positives.

20. A data professional calculates accuracy in logistic regression results. They have 87 true positives, 94 true negatives, and 222 total predictions. What is the calculation for accuracy?

21. A data professional calculates recall in logistic regression results. They have 99 true positives, 80 true negatives, 7 false positives, and 4 false negatives. What is the calculation for recall?

22. Fill in the blank: Maximum likelihood estimation is a technique for _____ the beta parameters that maximize the likelihood of a model producing the observed data.

23. For the binomial logistic regression linearity assumption, there should be a linear relationship between each X variable and what logit probability?

CONCLUSION – Logistic Regression