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()
