01-model.qmd

---
title: "01-model"
format: html
---

```{r setup, include=FALSE}
options(scipen = 999)
library(tidyverse)
library(tidymodels)

# update rcis if the package is not up-to-date - only way to access bechdel
# remotes::install_github("cis-ds/rcis")
library(rcis)

data("bechdel")
bechdel
```

# Your Turn 1

Run the chunk below and look at the output. Then, copy/paste the code and edit to create:

+ a decision tree model for classification 

+ that uses the `C5.0` engine. 

Save it as `tree_mod` and look at the object. What is different about the output?

*Hint: you'll need https://www.tidymodels.org/find/parsnip/*

```{r}
lr_mod <- logistic_reg() %>% 
  set_engine(engine = "glm") %>% 
  set_mode("classification")
lr_mod
```

```{r}

```

# Your Turn 2

Fill in the blanks. 

Use `initial_split()`, `training()`, and `testing()` to:

1. Split **bechdel** into training and test sets. Save the rsplit!

2. Extract the training data and fit your classification tree model.

3. Check the proportions of the `test` variable in each set.

Keep `set.seed(100)` at the start of your code.

*Hint: Be sure to remove every `_` before running the code!*

```{r}
set.seed(100) # Important!
bechdel_split  <- ________(bechdel, strata = test, prop = 3 / 4)
bechdel_train  <- ________(bechdel_split)
bechdel_test   <- ________(bechdel_split)
```

## Your Turn 3

Run the code below. What does it return?

```{r}
set.seed(100)
bechdel_folds <- vfold_cv(data = bechdel_train, v = 10, strata = test)
bechdel_folds
```

## Your Turn 4

Add a `autoplot()` to visualize the ROC AUC.

```{r}
tree_preds <- tree_mod %>% 
  fit_resamples(
    test ~ metascore + imdb_rating, 
    resamples = bechdel_folds,
    control = control_resamples(save_pred = TRUE)
  )

tree_preds %>% 
  collect_predictions() %>% 
  roc_curve(truth = test, .pred_Fail) %>%
  ________()
```