# Tune random forests for #TidyTuesday IKEA prices

**rstats | Julia Silge**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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This is the latest in my series of
screencasts demonstrating how to use the
tidymodels packages, from starting out with first modeling steps to tuning more complex models. Todayβs screencast walks through how to get started quickly with tidymodels via
usemodels functions for code scaffolding and generation, using this weekβs
`#TidyTuesday`

dataset on IKEA furniture prices. π

Here is the code I used in the video, for those who prefer reading instead of or in addition to video.

## Explore the data

Our modeling goal is to predict the price of IKEA furniture from other furniture characteristics like category and size. Letβs start by reading in the data.

library(tidyverse) ikea <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-03/ikea.csv")

How is the price related to the furniture dimensions?

ikea %>% select(X1, price, depth:width) %>% pivot_longer(depth:width, names_to = "dim") %>% ggplot(aes(value, price, color = dim)) + geom_point(alpha = 0.4, show.legend = FALSE) + scale_y_log10() + facet_wrap(~dim, scales = "free_x") + labs(x = NULL)

There are lots more great examples of #TidyTuesday EDA out there to explore on
Twitter! Letβs do a bit of data preparation for modeling. There are still lots of `NA`

values for furniture dimensions but we are going to *impute* those.

ikea_df <- ikea %>% select(price, name, category, depth, height, width) %>% mutate(price = log10(price)) %>% mutate_if(is.character, factor) ikea_df ## # A tibble: 3,694 x 6 ## price name category depth height width #### 1 2.42 FREKVENS Bar furniture NA 99 51 ## 2 3.00 NORDVIKEN Bar furniture NA 105 80 ## 3 3.32 NORDVIKEN / NORDVIKEN Bar furniture NA NA NA ## 4 1.84 STIG Bar furniture 50 100 60 ## 5 2.35 NORBERG Bar furniture 60 43 74 ## 6 2.54 INGOLF Bar furniture 45 91 40 ## 7 2.11 FRANKLIN Bar furniture 44 95 50 ## 8 2.29 DALFRED Bar furniture 50 NA 50 ## 9 2.11 FRANKLIN Bar furniture 44 95 50 ## 10 3.34 EKEDALEN / EKEDALEN Bar furniture NA NA NA ## # β¦ with 3,684 more rows

## Build a model

We can start by loading the tidymodels metapackage, splitting our data into training and testing sets, and creating resamples.

library(tidymodels) set.seed(123) ikea_split <- initial_split(ikea_df, strata = price) ikea_train <- training(ikea_split) ikea_test <- testing(ikea_split) set.seed(234) ikea_folds <- bootstraps(ikea_train, strata = price) ikea_folds ## # Bootstrap sampling using stratification ## # A tibble: 25 x 2 ## splits id ##

## 1 Bootstrap01 ## 2 Bootstrap02 ## 3 Bootstrap03 ## 4 Bootstrap04 ## 5 Bootstrap05 ## 6 Bootstrap06 ## 7 Bootstrap07 ## 8 Bootstrap08 ## 9 Bootstrap09 ## 10 Bootstrap10 ## # β¦ with 15 more rows

In this analysis, we are using a function from usemodels to provide scaffolding for getting started with tidymodels tuning. The two inputs we need are:

- a formula to describe our model
`price ~ .`

- our training data
`ikea_train`

library(usemodels) use_ranger(price ~ ., data = ikea_train) ## lots of options, like use_xgboost, use_glmnet, etc

The output that we get from the usemodels scaffolding sets us up for random forest tuning, and we can add just a few more feature engineering steps to take care of the numerous factor levels in the furniture name and category, βcleaningβ the factor levels, and imputing the missing data in the furniture dimensions. Then itβs time to tune!

library(textrecipes) ranger_recipe <- recipe(formula = price ~ ., data = ikea_train) %>% step_other(name, category, threshold = 0.01) %>% step_clean_levels(name, category) %>% step_knnimpute(depth, height, width) ranger_spec <- rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% set_mode("regression") %>% set_engine("ranger") ranger_workflow <- workflow() %>% add_recipe(ranger_recipe) %>% add_model(ranger_spec) set.seed(8577) doParallel::registerDoParallel() ranger_tune <- tune_grid(ranger_workflow, resamples = ikea_folds, grid = 11 )

The usemodels output required us to decide for ourselves on the `resamples`

and `grid`

to use; it provides sensible defaults for many options based on our data but we still need to use good judgment for some modeling inputs.

## Explore results

Now letβs see how we did. We can check out the best-performing models in the tuning results.

show_best(ranger_tune, metric = "rmse") ## # A tibble: 5 x 8 ## mtry min_n .metric .estimator mean n std_err .config #### 1 2 4 rmse standard 0.342 25 0.00211 Preprocessor1_Model10 ## 2 4 10 rmse standard 0.348 25 0.00234 Preprocessor1_Model05 ## 3 5 6 rmse standard 0.349 25 0.00267 Preprocessor1_Model06 ## 4 3 18 rmse standard 0.351 25 0.00211 Preprocessor1_Model01 ## 5 2 21 rmse standard 0.355 25 0.00197 Preprocessor1_Model08 show_best(ranger_tune, metric = "rsq") ## # A tibble: 5 x 8 ## mtry min_n .metric .estimator mean n std_err .config ## ## 1 2 4 rsq standard 0.714 25 0.00336 Preprocessor1_Model10 ## 2 4 10 rsq standard 0.704 25 0.00367 Preprocessor1_Model05 ## 3 5 6 rsq standard 0.703 25 0.00408 Preprocessor1_Model06 ## 4 3 18 rsq standard 0.698 25 0.00336 Preprocessor1_Model01 ## 5 2 21 rsq standard 0.694 25 0.00324 Preprocessor1_Model08

How did all the possible parameter combinations do?

autoplot(ranger_tune)

We can finalize our random forest workflow with the best performing parameters.

final_rf <- ranger_workflow %>% finalize_workflow(select_best(ranger_tune)) final_rf ## ββ Workflow ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ ## Preprocessor: Recipe ## Model: rand_forest() ## ## ββ Preprocessor ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ ## 3 Recipe Steps ## ## β step_other() ## β step_clean_levels() ## β step_knnimpute() ## ## ββ Model βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ ## Random Forest Model Specification (regression) ## ## Main Arguments: ## mtry = 2 ## trees = 1000 ## min_n = 4 ## ## Computational engine: ranger

The function `last_fit()`

**fits** this finalized random forest one last time to the training data and **evaluates** one last time on the testing data.

ikea_fit <- last_fit(final_rf, ikea_split) ikea_fit ## # Resampling results ## # Manual resampling ## # A tibble: 1 x 6 ## splits id .metrics .notes .predictions .workflow ##

## 1

The metrics in

`ikea_fit`

are computed using the testing data.collect_metrics(ikea_fit) ## # A tibble: 2 x 4 ## .metric .estimator .estimate .config #### 1 rmse standard 0.314 Preprocessor1_Model1 ## 2 rsq standard 0.769 Preprocessor1_Model1 The predictions in

`ikea_fit`

are also for the testing data.collect_predictions(ikea_fit) %>% ggplot(aes(price, .pred)) + geom_abline(lty = 2, color = "gray50") + geom_point(alpha = 0.5, color = "midnightblue") + coord_fixed()We can use the trained workflow from

`ikea_fit`

for prediction, or save it to use later.predict(ikea_fit$.workflow[[1]], ikea_test[15, ]) ## # A tibble: 1 x 1 ## .pred #### 1 2.72 Lastly, letβs learn about feature importance for this model using the vip package. For a ranger model, we do need to go back to the model specification itself and update the engine with

`importance = "permutation"`

in order to compute feature importance. This means fitting the model one more time.library(vip) imp_spec <- ranger_spec %>% finalize_model(select_best(ranger_tune)) %>% set_engine("ranger", importance = "permutation") workflow() %>% add_recipe(ranger_recipe) %>% add_model(imp_spec) %>% fit(ikea_train) %>% pull_workflow_fit() %>% vip(aesthetics = list(alpha = 0.8, fill = "midnightblue"))Toleave a commentfor the author, please follow the link and comment on their blog:rstats | Julia Silge.

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