data analysis with r coursera week 5 quiz answers

Practice Quiz

1. When evaluating models, what is the term used to describe a situation where a model fits the training data very well but performs poorly when predicting new data?

  • Small dataset 
  • Underfit
  • Cross validation
  • Overfit

2. An underfit model is said to have which of the following?

  • High generalizability
  • High complexity
  • High bias  
  • High variance

3. What does regularization introduce into a model that results in a drop in variance?

  • Lambda
  • Feature/variable
  • Noise
  • Complexity

4. Complete the sentence: When tuning a model, a grid search attempts to find the value of a parameter that has the smallest ______________.

  • Variance 
  • Lambda
  • Bias
  • Error

Graded Quiz

5. Which situations are helped by using the cross-validation method to train your model? Select two answers.

  • Working with models with large amounts of data.
  • Working with models that are underfit. 
  • Determining if a model can be generalized for a broader group. 

  • Working with models with small amounts of data. 

6. What is a strategy you can employ to address an underfit model?

  • Increase model complexity.
  • Use regularization.
  • Reduce the number of features in the training data. 
  • Reduce model complexity. 

7. What is the difference between Ridge and Lasso regression?

  • Lasso regression penalizes the sum of the absolute values of the coefficients while Ridge regression penalizes the sum of squared coefficients. 
  • Lasso regression increases or decreases the value of Lambda to penalize complex models more or less. 
  • Ridge regression penalizes the sum of the absolute values of the coefficients while Lasso regression penalizes the sum of squared coefficients. 
  • There is no major difference between Ridge and Lasso regression. 

8. Which tidymodels function do you use to create the grid for a grid search?

  • grid_regular()  
  • add_model() 
  • tune()
  • tune_grid()

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