11. Which of the following are regularized regression techniques? Select all that apply.

  • Lasso regression
  • Ridge regression
  • Elastic-net regression
  • F-test regression

Weekly challenge 3

12. Multiple linear regression estimates the linear relationship between one continuous dependent variable and how many independent variables?

  • One
  • Two or more
  • Zero
  • Two

13. Fill in the blank: One hot encoding is a data transformation technique that turns one categorical variable into several _____ variables.

  • dependent
  • overfit
  • binary
  • independent

14. Fill in the blank: The no multicollinearity assumption states that no two _____ variables can be highly correlated with each other.

  • independent
  • dependent
  • continuous
  • categorical

15. What term represents the relationship for how two variables’ values affect each other?

  • Feature selection term
  • Interaction term
  • Linearity term
  • Underfitting term

16. A data professional uses an evaluation metric that penalizes unnecessary explanatory variables. Which metric are they using?

  • Link function
  • Ordinary least squares
  • Adjusted R squared
  • Holdout sampling

17. What stepwise variable selection process begins with the full model with all possible independent variables?

  • Backward elimination
  • Extra-sum-of-squares F-test
  • Overfit selection
  • Forward selection

18. A data professional creates a model that allows for flexibility and complexity, so it learns from existing data. What quality does this model have?

  • Variance
  • Bias
  • Elimination
  • Selection

Shuffle Q/A 2

19. What regularization technique completely removes variables that are less important to predicting the y variable of interest?

  • Lasso regression
  • Ridge regression
  • Elastic net regression
  • Independent regression

20. What technique estimates the linear relationship between one continuous dependent variable and two or more independent variables?

  • Interaction terms
  • Simple linear regression
  • Multiple linear regression
  • One hot encoding

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