# Overview

Due by 11:59pm on November 1st.

# Accessing the hw07 repository

Go here and find your copy of the hw07 repository. It follows the naming convention hw07-<USERNAME>. Clone the repository to your computer.

# Part 1: Student debt

Median student debt in the United States has increased substantially over the past twenty years.

rcis::scorecard includes debt, which reports the median debt of students after leaving school in 2019.

For all models, exclude unitid and name as predictors. These serve as id variables in the data set and uniquely identify each observation. They are not useful in predicting an outcome of interest.
1. Split scorecard into training and test sets with 75% allocated to training and 25% allocated to testing.
2. Split the training set into 10 distinct folds for cross-validation.
3. Estimate a linear regression model to predict debt as a function of all the other variables in the dataset except for state, using 10-fold cross-validation. Report the RMSE for the model.1
4. Estimate a decision tree model to predict debt using 10-fold cross-validation. Use the rpart engine. Report the RMSE for the model.

## For those looking to stretch themselves

Estimate one or more models which utilize some aspect of feature engineering or model tuning. Discuss the process you used to estimate the model and report on its performance.

# Part 2: Predicting attitudes towards marijuana legalization

The General Social Survey is a biannual survey of the American public.

Over the past twenty years, American attitudes towards marijuana have softened extensively. In the early 2010s, the number of Americans who believed marijuana should be legal began to outnumber those who thought it should not be legal.

rcis::gss contains a selection of variables from the 2018 GSS. The outcome of interest grass is a factor variable coded as either "legal" (respondent believes marijuana should be legal) or "not legal" (respondent believes marijuana should not be legal).

Make sure you have the most recent version of rcis. If the version is less than 0.2.6, reinstall rcis using the most recent version.

if (packageVersion("rcis") < 0.2.6) {
remotes::install_github("cis-ds/rcis")
}

For all models, exclude id and wtss as predictors. These serve as id variables in the data set and uniquely identify each observation. They are not useful in predicting an outcome of interest.
1. Split gss into training and test sets with 75% allocated to training and 25% allocated to testing.

2. Estimate a logistic regression model to predict grass as a function of age, degree, happy, partyid, and sex,. Implement 10-fold cross-validation. Report the accuracy of the model.

3. Estimate a random forest model to predict grass as a function of all the other variables in the dataset (except id and wtss). In order to do this, you need to impute missing values for all the predictor columns. This means replacing missing values (NA) with plausible values given what we know about the other observations.

• Remove rows with an NA for grass - we want to omit observations with missing values for outcomes, not impute them
• Use median imputation for numeric predictors
• Use modal imputation for nominal predictors

Implement 10-fold cross-validation. Report the accuracy of the model.

4. Estimate a $5$-nearest neighbors model to predict grass. Use recipes to prepare the data set for training this model (e.g. scaling and normalizing variables, ensuring all predictors are numeric). Be sure to also perform the same preprocessing as for the random forest model. Make sure your step order is correct for the recipe. Implement 10-fold cross-validation. Report the accuracy of the model.

5. Estimate a ridge logistic regression model to predict grass.2 Use the same recipe as for the $5$-nearest neighbors model. Implement 10-fold cross-validation, and utilize the same recipe as for the $k$-nearest neighbors model. Report the accuracy of the model.

6. Revisit the random forest model used previously. This time, implement hyperparameter tuning over the mtry and min_n to find the optimal settings. Use at least ten combinations of hyperparameter values. Report the best five combinations of values.

7. Select the best performing model. Train that recipe/model using the full training set and report the accuracy/ROC AUC using the held-out test set of data. Visualize the ROC curve.

Documentation for the other predictors (if the variable is not clearly coded) can be viewed here. You can also run ?gss to open a documentation file in R.

# Submit the assignment

Your assignment should be submitted as a set of two Quarto documents using the gfm (GitHub Flavored Markdown) format. Follow instructions on homework workflow.

# Rubric

Needs improvement: Cannot get code to run or is poorly documented. No documentation in the README file. Severe misinterpretations of the results. Overall a shoddy or incomplete assignment.

Satisfactory: Solid effort. Hits all the elements. No clear mistakes. Easy to follow (both the code and the output). Nothing spectacular, either bad or good.

Excellent: Interpretation is clear and in-depth. Accurately interprets the results, with appropriate caveats for what the technique can and cannot do. Code is reproducible. Writes a user-friendly README file. Implements appropriate visualization techniques for the statistical model. Results are presented in a clear and intuitive manner.

1. View the documentation for yardstick to find the appropriate function for RMSE. ↩︎

2. logistic_reg(penalty = .01, mixture = 0) ↩︎