12-workflows-solutions.Rmd

---
title: "Workflows - Solutions"
output: html_notebook
editor_options: 
  chunk_output_type: inline
---

```{r setup, include=FALSE}
library(tidyverse)
library(AmesHousing)
library(tidymodels)

ames <- make_ames() %>% 
  dplyr::select(-matches("Qu"))

set.seed(100)
ames_split <- initial_split(ames)
ames_train <- training(ames_split)
ames_test <- testing(ames_split)
```


# Your Turn 1

Build a workflow that uses a linear model to predict `Sale_Price` with `Bedrooms_AbvGr`, `Full_Bath` and `Half_Bath` in `ames`. Save it as `bb_wf`.

```{r}
lm_spec <-
  linear_reg() %>%
  set_engine("lm")

bb_wf <-
  workflow() %>%
  add_formula(Sale_Price ~ Bedroom_AbvGr + Full_Bath + Half_Bath) %>%
  add_model(lm_spec)
```


# Your Turn 2

Test the linear model that predicts `Sale_Price` with everything else in `ames`. Use cross validation to estimate the RMSE.

1. Create a new workflow by updating `bb_wf`.

1. Use `vfold_cv()` to create a 10-fold cross validation of `ames_train`.

1. Fit the workflow

1. Use `collect_metrics()` to estimate the RMSE.

```{r}
all_wf <- 
  bb_wf %>% 
  update_formula(Sale_Price ~ .)

ames_folds <- vfold_cv(ames_train, v = 10)

fit_resamples(all_wf, resamples = ames_folds) %>%
  collect_metrics()
```


# Your Turn 3

Fill in the blanks to test the regression tree model that predicts `Sale_Price` with _everything else in `ames`_ on `ames_folds`. What RMSE do you get?

*Hint: Create a new workflow by updating `all_wf`.*

```{r}
rt_spec <- 
  decision_tree() %>%          
  set_engine(engine = "rpart") %>% 
  set_mode("regression")

rt_wf <- 
  all_wf %>% 
  update_model(rt_spec)

fit_resamples(rt_wf, resamples = ames_folds) %>% 
  collect_metrics()
```


# Your Turn 4

But what about the predictions of our model? Save the fitted object from your regression tree, and use `collect_predictions()` to see the predictions generated from the test data.

```{r}
all_fitwf <- 
  fit_resamples(
    rt_wf,
    resamples = ames_folds,
    control = control_resamples(save_pred = TRUE)
)

all_fitwf %>%
  collect_predictions()
```