Building better models

So far, in the labs, we’ve learned how to make predictions using the line of best fit
- Which we also call 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

Start by loading the movie data and split it into two sets (See Lab 4C for help). Remember to use set.seed.
- A set named training that includes 75% of the data.
- A set named testing that includes the remaining 25%.
Create a linear model, using the training data, that predicts gross using runtime.
- Compute the MSE of the model by making predictions for the testing data.
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?

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

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

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

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