05-Workflows.Rmd

---
title: "05-Workflows"
output: html_document
---

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

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)

fit_data <- function(object, model, data, ...) {
  if (inherits(object, "formula")) {
    object <- add_model(add_formula(workflow(), object, blueprint = hardhat::default_formula_blueprint(indicators = FALSE, ...)))
  }
  fit(object, data, ...)
}

fit_split <- function(object, model, split, ...) {
  if (inherits(object, "formula")) {
    object <- add_model(add_formula(workflow(), object, blueprint = hardhat::default_formula_blueprint(indicators = FALSE)), model)
  }
  tune::last_fit(object, split, ...)
}

# pca_rec <- 
#   recipe(Sale_Price ~ ., data = ames) %>%
#     step_novel(all_nominal()) %>%
#     step_dummy(all_nominal()) %>%
#     step_zv(all_predictors()) %>%
#     step_center(all_predictors()) %>%
#     step_scale(all_predictors()) %>%
#     step_pca(all_predictors(), num_comp = 5)
```

# 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 <- 
  ___________ %>% 
  ___________ %>% 
  ___________

bb_wf <- 
  ___________ %>% 
  ___________ %>% 
  ___________
```


# Your Turn 2

Test the linear model that predicts `Sale_Price` with _everything else in `ames`_ on `ames_split`. What RMSE do you get?

Hint: Create a new workflow by updating `bb_wf`.

```{r}
all_wf <- 
  bb_wf %>% 
  ___________

___________(all_wf, split = ___________) %>% 
  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_split`. What RMSE do you get?

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

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

rt_wf <- 
  ___________ %>% 
  ___________

___________ %>% 
  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 <- ___________

___________ %>% 
  ___________
```