This model is big enough for all of us!

Lab 4F

Directions: Follow along with the slides, completing the questions in blue on your computer, and answering the questions in red in your journal.

Space, Click, Right Arrow or swipe left to move to the next slide.

Building better models

So far, in the labs, we’ve learned how to make predictions using the line of best fit, also known as linear models or regression models.
We’ve also learned how to measure our model’s prediction accuracy by cross-validation.
In this lab, we’ll investigate the following question:

Will including more variables in our model improve its predictions?

Divide & Conquer

(1) Start by loading the movie data and write and run code splitting it into two sets (see Lab 4C for help).
- A set named training that includes 75% of the data.
- A set named test that includes the remaining 25%.
- Remember to use set.seed.
(2) Write and run code creating a linear model, using the training data, that predicts gross using runtime.
- (3) Write and run code calculating the MSE of the model by making predictions for the test data.
(4) Do you think that a movie’s runtime is the only factor that goes into how much a movie will make? What else might affect a movie’s gross?

Including more info

Data scientists often find that including more relevant information in their models leads to better predictions.
(5) Fill in the blanks below to predict gross using runtime and reviews_num.

lm(____ ~ ____ + ____, data = training)

(6) Does this new model make more or less accurate predictions? Describe the process you used to arrive at your conclusion.
(7) Write down the code you would use to include a 3rd variable, of your choosing, in your lm().

On your own

(8) Write down which other variables in the movie data you think would help you make better predictions.
- (9) Are there any variables that you think would not improve our predictions?
(10) Write and run code creating a model for all of the variables you think are relevant.
- (11) Assess whether your model makes more accurate predictions for the test data than the model that included only runtime and reviews_num.
(12) With your neighbors, determine which combination of variables leads to the best predictions for the test data.