Each year, the American Academy of Motion Picture Arts and Sciences recognizes excellence in the film industry

Question:

Each year, the American Academy of Motion Picture Arts and Sciences recognizes excellence in the film industry by honoring directors, actors, and writers with awards (called “Oscars”) in different categories. The most notable of these awards is the Oscar for Best Picture. Data has been collected on a sample of movies nominated for the Best Picture Oscar. The variables include total number of Oscar nominations across all award categories, number of Golden Globe awards won (the Golden Globe award show precedes the Academy Awards), the genre of the movie, and whether or not the movie won the Best Picture Oscar award.

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Apply logistic regression with lasso regularization to classify winners of the Best Picture Oscar by using Winner as the target (or response) variable. Use 100% of the data for training and validation (do not use any data as a test set).

a. Determine the lasso regularization penalty that maximizes AUC in a validation procedure.

b. For the level of lasso regularization identified in part (a), what are the values of the intercept and variable coefficients in this final model? Interpret the coefficients.
Does the lasso regularization eliminate any variables?

c. Note that each year there is only one winner of the Best Picture Oscar (1 winner in each of the 38 years). Knowing this, examine the confusion matrix and explain what is wrong with classifying a movie based on a cutoff value?

d. What is the best way to use the model to predict the annual winner?

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