taxi_train
#> # A tibble: 8,000 × 7
#> tip distance company local dow month hour
#> <fct> <dbl> <fct> <fct> <fct> <fct> <int>
#> 1 yes 17.2 Chicago Independents no Thu Feb 16
#> 2 yes 0.88 City Service yes Thu Mar 8
#> 3 yes 18.1 other no Mon Feb 18
#> 4 yes 12.2 Chicago Independents no Sun Mar 21
#> 5 yes 0.94 Sun Taxi yes Sat Apr 23
#> 6 yes 17.5 Flash Cab no Fri Mar 12
#> 7 yes 17.7 other no Sun Jan 6
#> 8 yes 1.85 Taxicab Insurance Agency Llc no Fri Apr 12
#> 9 yes 0.53 Sun Taxi no Tue Mar 18
#> 10 yes 6.65 Taxicab Insurance Agency Llc no Sun Apr 11
#> # ℹ 7,990 more rowsExtras - Recipes
Introduction to tidymodels
Looking at the predictors
Working with other models
Some models can’t handle non-numeric data
- Linear Regression
- K Nearest Neighbors
Some models struggle if numeric predictors aren’t scaled
- K Nearest Neighbors
- Anything using gradient descent
Types of needed preprocessing
Do qualitative predictors require a numeric encoding?
Should columns with a single unique value be removed?
Does the model struggle with missing data?
Does the model struggle with correlated predictors?
Should predictors be centered and scaled?
Is it helpful to transform predictors to be more symmetric?
Two types of preprocessing
Two types of preprocessing
General definitions
- Data preprocessing are the steps that you take to make your model successful.
- Feature engineering are what you do to the original predictors to make the model do the least work to perform great.
Working with dates
Datetime variables are automatically converted to an integer if given as a raw predictor. To avoid this, it can be re-encoded as:
- Days since a reference date
- Day of the week
- Month
- Year
- Leap year
- Indicators for holidays
Your turn
What other transformations could we do with the raw time variable?
Remember that the transformations are tied to the specific modeling problem.
03:00
Two types of transformations
Static
- Square root, log, inverse
- Dummies for known levels
- Date time extractions
Trained
- Centering & scaling
- Imputation
- PCA
- Anything for unknown factor levels
Trained methods need to calculate sufficient information to be applied again.
The recipes package
- Modular + extensible
- Works well with pipes ,
|>and%>% - Deferred evaluation
- Isolates test data from training data
- Can do things formulas can’t
How to write a recipe
taxi_rec <- recipe(tip ~ ., data = taxi_train) %>%
step_unknown(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors()) %>%
step_log(distance, offset = 0.5) %>%
step_normalize(all_numeric_predictors())
How to write a recipe
taxi_rec <- recipe(tip ~ ., data = taxi_train) %>%
step_unknown(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors()) %>%
step_log(distance, offset = 0.5) %>%
step_normalize(all_numeric_predictors())
Start by calling recipe() to denote the data source and variables used.
How to write a recipe
taxi_rec <- recipe(tip ~ ., data = taxi_train) %>%
step_unknown(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors()) %>%
step_log(distance, offset = 0.5) %>%
step_normalize(all_numeric_predictors())
Specify what actions to take by adding step_*()s.
How to write a recipe
taxi_rec <- recipe(tip ~ ., data = taxi_train) %>%
step_unknown(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors()) %>%
step_log(distance, offset = 0.5) %>% step_normalize(all_numeric_predictors())
Use {tidyselect} and recipes-specific selectors to denote affected variables.
Using a recipe
taxi_rec <- recipe(tip ~ ., data = taxi_train) %>%
step_unknown(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors()) %>%
step_log(distance, offset = 0.5) %>% step_normalize(all_numeric_predictors())
Save the recipe we like so that we can use it in various places, e.g., with different models.
Using a recipe with workflows
Recipes are typically combined with a model in a workflow() object:
taxi_wflow <- workflow() %>%
add_recipe(taxi_rec) %>%
add_model(linear_reg())
Recipes are estimated
Every preprocessing step in a recipe that involved calculations uses the training set. For example:
- Levels of a factor
- Determination of zero-variance
- Normalization
- Feature extraction
Once a recipe is added to a workflow, this occurs when fit() is called.
Debugging a recipe
- Typically, you will want to use a workflow to estimate and apply a recipe.
- If you have an error and need to debug your recipe, the original recipe object (e.g.
taxi_rec) can be estimated manually with a function calledprep(). It is analogous tofit(). See TMwR section 16.4.
- Another function,
bake(), is analogous topredict(), and gives you the processed data back.
Your turn
Take the recipe and prep() then bake() it to see what the resulting data set looks like.
Try removing steps to see how the result changes.
05:00
Printing a recipe
taxi_rec
#>
#> ── Recipe ────────────────────────────────────────────────────────────
#>
#> ── Inputs
#> Number of variables by role
#> outcome: 1
#> predictor: 6
#>
#> ── Operations
#> • Unknown factor level assignment for: all_nominal_predictors()
#> • Dummy variables from: all_nominal_predictors()
#> • Zero variance filter on: all_predictors()
#> • Log transformation on: distance
#> • Centering and scaling for: all_numeric_predictors()Prepping a recipe
prep(taxi_rec)
#>
#> ── Recipe ────────────────────────────────────────────────────────────
#>
#> ── Inputs
#> Number of variables by role
#> outcome: 1
#> predictor: 6
#>
#> ── Training information
#> Training data contained 8000 data points and no incomplete rows.
#>
#> ── Operations
#> • Unknown factor level assignment for: company, ... | Trained
#> • Dummy variables from: company, local, dow, month | Trained
#> • Zero variance filter removed: company_unknown, ... | Trained
#> • Log transformation on: distance | Trained
#> • Centering and scaling for: distance and hour, ... | TrainedBaking a recipe
prep(taxi_rec) %>%
bake(new_data = taxi_train)
#> # A tibble: 8,000 × 19
#> distance hour tip company_City.Service company_Flash.Cab company_Sun.Taxi company_Taxi.Affiliatio…¹ company_Taxicab.Insu…² company_other local_no dow_Mon dow_Tue dow_Wed dow_Thu dow_Fri dow_Sat
#> <dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1.38 0.418 yes -0.366 -0.333 -0.403 -0.450 -0.379 -0.609 0.484 -0.396 -0.441 -0.461 2.01 -0.428 -0.317
#> 2 -0.729 -1.42 yes 2.73 -0.333 -0.403 -0.450 -0.379 -0.609 -2.07 -0.396 -0.441 -0.461 2.01 -0.428 -0.317
#> 3 1.42 0.877 yes -0.366 -0.333 -0.403 -0.450 -0.379 1.64 0.484 2.53 -0.441 -0.461 -0.497 -0.428 -0.317
#> 4 1.11 1.57 yes -0.366 -0.333 -0.403 -0.450 -0.379 -0.609 0.484 -0.396 -0.441 -0.461 -0.497 -0.428 -0.317
#> 5 -0.694 2.03 yes -0.366 -0.333 2.48 -0.450 -0.379 -0.609 -2.07 -0.396 -0.441 -0.461 -0.497 -0.428 3.15
#> 6 1.39 -0.502 yes -0.366 3.01 -0.403 -0.450 -0.379 -0.609 0.484 -0.396 -0.441 -0.461 -0.497 2.34 -0.317
#> 7 1.40 -1.88 yes -0.366 -0.333 -0.403 -0.450 -0.379 1.64 0.484 -0.396 -0.441 -0.461 -0.497 -0.428 -0.317
#> 8 -0.289 -0.502 yes -0.366 -0.333 -0.403 -0.450 2.64 -0.609 0.484 -0.396 -0.441 -0.461 -0.497 2.34 -0.317
#> 9 -0.971 0.877 yes -0.366 -0.333 2.48 -0.450 -0.379 -0.609 0.484 -0.396 2.27 -0.461 -0.497 -0.428 -0.317
#> 10 0.631 -0.732 yes -0.366 -0.333 -0.403 -0.450 2.64 -0.609 0.484 -0.396 -0.441 -0.461 -0.497 -0.428 -0.317
#> # ℹ 7,990 more rows
#> # ℹ abbreviated names: ¹company_Taxi.Affiliation.Services, ²company_Taxicab.Insurance.Agency.Llc
#> # ℹ 3 more variables: month_Feb <dbl>, month_Mar <dbl>, month_Apr <dbl>Tidying a recipe
Once a recipe as been estimated, there are various bits of information saved in it.
- The
tidy()function can be used to get specific results from the recipe.
Your turn
Take a prepped recipe and use the tidy() function on it.
Use the number argument to inspect different steps.
05:00
Tidying a recipe
prep(taxi_rec) %>%
tidy()
#> # A tibble: 5 × 6
#> number operation type trained skip id
#> <int> <chr> <chr> <lgl> <lgl> <chr>
#> 1 1 step unknown TRUE FALSE unknown_NTmu5
#> 2 2 step dummy TRUE FALSE dummy_cT3Uy
#> 3 3 step zv TRUE FALSE zv_z22dk
#> 4 4 step log TRUE FALSE log_QQ1iw
#> 5 5 step normalize TRUE FALSE normalize_3cTJbTidying a recipe
prep(taxi_rec) %>%
tidy(number = 2)
#> # A tibble: 20 × 3
#> terms columns id
#> <chr> <chr> <chr>
#> 1 company City Service dummy_cT3Uy
#> 2 company Flash Cab dummy_cT3Uy
#> 3 company Sun Taxi dummy_cT3Uy
#> 4 company Taxi Affiliation Services dummy_cT3Uy
#> 5 company Taxicab Insurance Agency Llc dummy_cT3Uy
#> 6 company other dummy_cT3Uy
#> 7 company unknown dummy_cT3Uy
#> 8 local no dummy_cT3Uy
#> 9 local unknown dummy_cT3Uy
#> 10 dow Mon dummy_cT3Uy
#> 11 dow Tue dummy_cT3Uy
#> 12 dow Wed dummy_cT3Uy
#> 13 dow Thu dummy_cT3Uy
#> 14 dow Fri dummy_cT3Uy
#> 15 dow Sat dummy_cT3Uy
#> 16 dow unknown dummy_cT3Uy
#> 17 month Feb dummy_cT3Uy
#> 18 month Mar dummy_cT3Uy
#> 19 month Apr dummy_cT3Uy
#> 20 month unknown dummy_cT3UyUsing a recipe in tidymodels
The recommended way to use a recipe in tidymodels is to use it as part of a workflow().
When used in this way, you don’t need to worry about prep() and bake() as it is handled for you.