regression analysis: simplify complex data relationships coursera weekly challenges 5 answers

Test your knowledge: Foundations of logistic regression

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

  • Connector
  • Classifier
  • Claimer
  • Codifier

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

  • Results package
  • Matrix package
  • Dashboard package
  • Seaborn package

Test your knowledge: Logistics regression with Python

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

  • Linearity
  • No multicollinearity
  • Independent observations
  • Homoscedasticity

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

  • True
  • False

5. 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.

  • reduce
  • maximize
  • balance
  • control

Test your knowledge: Interpret logistic regression results

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

  • Precision
  • Errors
  • Labels
  • Validity

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

  • Validity
  • Precision
  • Accuracy
  • Recall

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

  • P-value
  • Maximum likelihood estimation
  • Confidence intervals
  • Logit

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Test your knowledge: Compare regression models

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

  • Single linear regression
  • Hypothesis testing
  • Multiple linear regression
  • Binomial logistic regression

10. 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.

  • Precision
  • R squared
  • P-value
  • Confusion matrices

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