</ Flexible
feature engineering
using {recipes} />

Emil Hvitfeldt

What is
feature engineering?

What are features?

I tend to think of a feature as some representation of a predictor that will be used in a model.

Old-school features:

• Interactions
• Polynomial expansions/splines
• PCA feature extraction

“Feature engineering” sounds pretty cool, but let’s take a minute to talk about preprocessing data.

</ 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

Some models can’t handle non-numeric data (missing data)

• Linear Regression
• K Nearest Neighbors

Some models are fine with categorical variables

• most tree based models

Some models struggle if numeric variables aren’t scaled

• K Nearest Neighbors
• Anything using gradient descent

</ Working with dates

Consider a datetime field. If given as a raw predictor, it is converted to an integer.

It can be re-encoded as:

• Days since a reference date
• Day of the week
• Month
• Year
• Indicators for holidays

static

• sqrt, log, inverse
• dummies with known levels
• date time extractions

trained

• centering & scaling
• Imputation
• PCA
• anything for unknown levels

Trained methods needs to calculate sufficient information to be applied again

</ Considerations

if all methods are static, they can be done ahead of time

• good for computational methods
  • BERT
• bad for fast and expanding methods
  • feature hashing

anything after a trained transformation needs to be done within the modeling process

%>% {recipes} package

extensible framework for pipeable sequences of feature engineering steps provides preprocessing tools to be applied to data

• Modular + extensible
• pipeable
• Deferred evaluation
• Isolates test data from training data
• Can do things formulas can’t

</ What is a recipe?

recipe(sale_price ~ ., data = ames_training) |>
  step_unknown(all_nominal_predictors()) |>
  step_other(all_nominal_predictors()) |>
  step_dummy(all_nominal_predictors()) |>
  step_date(date_sold, features = c(“month”, “dow”, “week”)) |>
  step_zv(all_predictors()) |>
  step_normalize(all_numeric_predictors())

</ What is a recipe?

recipe(sale_price ~ ., data = ames_training) |>
  step_unknown(all_nominal_predictors()) |>
  step_other(all_nominal_predictors()) |>
  step_dummy(all_nominal_predictors()) |>
  step_date(date_sold, features = c(“month”, “dow”, “week”)) |>
  step_zv(all_predictors()) |>
  step_normalize(all_numeric_predictors())

Start by calling recipe() to denote the data source and variables used

</ What is a recipe?

recipe(sale_price ~ ., data = ames_training) |>
  step_unknown(all_nominal_predictors()) |>
  step_other(all_nominal_predictors()) |>
  step_dummy(all_nominal_predictors()) |>
  step_date(date_sold, features = c(“month”, “dow”, “week”)) |>
  step_zv(all_predictors()) |>
  step_normalize(all_numeric_predictors())

specifying what actions to take by adding step_*()s

</ What is a recipe?

recipe(sale_price ~ ., data = ames_training) |>
  step_unknown(all_nominal_predictors()) |>
  step_other(all_nominal_predictors()) |>
  step_dummy(all_nominal_predictors()) |>
  step_date(date_sold, features = c(“month”, “dow”, “week”)) |>
  step_zv(all_predictors()) |>
  step_normalize(all_numeric_predictors())

using {tidyselect} and recipes specific selectors to denote affected variables

</ What is a recipe?

recipe(sale_price ~ ., data = ames_training) |>
  step_unknown(all_nominal_predictors()) |>
  step_other(all_nominal_predictors()) |>
  step_dummy(all_nominal_predictors()) |>
  step_date(date_sold, features = c(“month”, “dow”, “week”)) |>
  step_zv(all_predictors()) |>
  step_normalize(all_numeric_predictors())

many steps have options to modify behavior

</ Using a recipe

recipes are can be used with {workflows} to “combine” it with a model

wf_spec <- workflow() |>
  add_recipe(rec_spec) |>
  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 a recipe is added to a workflow,
this occurs when fit() is called.

</ types of steps - trained

recipe(sale_price ~ ., data = ames_training) |>
  step_unknown(all_nominal_predictors()) |>
  step_other(all_nominal_predictors()) |>
  step_dummy(all_nominal_predictors()) |>
  step_date(date_sold, features = c(“month”, “dow”, “week”)) |>
  step_zv(all_predictors()) |>
  step_normalize(all_numeric_predictors())

Levels not found in tranining data set are set to “unseen”

</ types of steps - trained

recipe(sale_price ~ ., data = ames_training) |>
  step_unknown(all_nominal_predictors()) |>
  step_other(all_nominal_predictors()) |>
  step_dummy(all_nominal_predictors()) |>
  step_date(date_sold, features = c(“month”, “dow”, “week”)) |>
  step_zv(all_predictors()) |>
  step_normalize(all_numeric_predictors())

records which levels are seen in training data set

</ types of steps - trained

recipe(sale_price ~ ., data = ames_training) |>
  step_unknown(all_nominal_predictors()) |>
  step_other(all_nominal_predictors()) |>
  step_dummy(all_nominal_predictors()) |>
  step_date(date_sold, features = c(“month”, “dow”, “week”)) |>
  step_zv(all_predictors()) |>
  step_normalize(all_numeric_predictors())

records which variables had zero variance

</ types of steps - trained

recipe(sale_price ~ ., data = ames_training) |>
  step_unknown(all_nominal_predictors()) |>
  step_other(all_nominal_predictors()) |>
  step_dummy(all_nominal_predictors()) |>
  step_date(date_sold, features = c(“month”, “dow”, “week”)) |>
  step_zv(all_predictors()) |>
  step_normalize(all_numeric_predictors())

records mean and sd of variables

</ types of steps - static

recipe(sale_price ~ ., data = ames_training) |>
  step_unknown(all_nominal_predictors()) |>
  step_other(all_nominal_predictors()) |>
  step_dummy(all_nominal_predictors()) |>
  step_date(date_sold, features = c(“month”, “dow”, “week”)) |>
  step_zv(all_predictors()) |>
  step_normalize(all_numeric_predictors())

these steps provide static transformations, and could thus be done outside before the recipe

</ Extensive role selection

All steps use {tidyselect} to select variables

… |>
step_pca(BsmtFin_SF_1, BsmtFin_SF_2, Bsmt_Unf_SF,
         Total_Bsmt_SF, First_Flr_SF, Second_Flr_SF,
         Wood_Deck_SF, Open_Porch_SF,
         threshold = 0.8) |>
…

variables can be written out one by one

</ Extensive role selection

All steps use {tidyselect} to select variables

… |>
step_pca(contains(“SF”), threshold = 0.8) |>
…

using tidyselect::contains()

</ Extensive role selection

All steps use {tidyselect} to select variables

… |>
step_pca(contains(“SF”), -starts_with(“Bsmt”),
         threshold = 0.8) |>
…

or combine multiple {tidyselect} selectors

</ Useful selectors - tidyselect

• starts_with()
• ends_with()
• contains()
• matches()
• num_range()
• one_of()
• any_of()

</ Useful selectors - recipes

In addition to all_predictors() and all_outcomes()

• all_numeric()
  • all_double()
  • all_integer()
• all_logical()
• all_date()
• all_datetime()

• all_nominal()
  • all_string()
  • all_factor()
  • all_unordered()
  • all_ordered()

all have *_predictors() variants

</ Tidying a recipe

We can use prep() to “train” a recipe

rec <- recipe(sale_price ~ ., data = ames_training) |>
  step_unknown(all_nominal_predictors()) |>
  step_other(all_nominal_predictors()) |>
  step_dummy(all_nominal_predictors()) |>
  step_nzv(all_predictors()) |>
  step_normalize(all_numeric_predictors())

rec_prepped <- prep(rec)

</ Tidying a recipe

running tidy() reveals the steps and basic information

rec_prepped |>
  tidy()

# A tibble: 5 × 6
  number operation type      trained skip  id             
   <int> <chr>     <chr>     <lgl>   <lgl> <chr>          
1      1 step      unknown   TRUE    FALSE unknown_n939d  
2      2 step      other     TRUE    FALSE other_zfonZ    
3      3 step      dummy     TRUE    FALSE dummy_pdHMv    
4      4 step      nzv       TRUE    FALSE nzv_RUieL      
5      5 step      normalize TRUE    FALSE normalize_Bp5vK

</ Tidying a recipe

you can use number or id to select a step

rec_prepped |>
  tidy(number = 4) # id = "nzv_RUieL"

# A tibble: 32 × 2
   terms              id       
   <chr>              <chr>    
 1 BsmtFin_SF_2       nzv_RUieL
 2 Kitchen_AbvGr      nzv_RUieL
 3 Open_Porch_SF      nzv_RUieL
 4 Enclosed_Porch     nzv_RUieL
 5 Three_season_porch nzv_RUieL
 6 Screen_Porch       nzv_RUieL
 7 Pool_Area          nzv_RUieL
 8 Misc_Val           nzv_RUieL
 9 Street_other       nzv_RUieL
10 Lot_Shape_other    nzv_RUieL
# ℹ 22 more rows

</ Tidying a recipe

you can use number or id to select a step

rec_prepped |>
  tidy(number = 3) # id = "dummy_pdHMv"

# A tibble: 98 × 3
   terms       columns                              id         
   <chr>       <chr>                                <chr>      
 1 MS_SubClass One_and_Half_Story_Finished_All_Ages dummy_pdHMv
 2 MS_SubClass Two_Story_1946_and_Newer             dummy_pdHMv
 3 MS_SubClass One_Story_PUD_1946_and_Newer         dummy_pdHMv
 4 MS_SubClass other                                dummy_pdHMv
 5 MS_Zoning   Residential_Medium_Density           dummy_pdHMv
 6 MS_Zoning   other                                dummy_pdHMv
 7 Street      other                                dummy_pdHMv
 8 Alley       other                                dummy_pdHMv
 9 Lot_Shape   Slightly_Irregular                   dummy_pdHMv
10 Lot_Shape   other                                dummy_pdHMv
# ℹ 88 more rows

</ Extension packages

• tidymodels/textrecipes
• tidymodels/embed
• tidymodels/themis
• business-science/timetk
• tidymodels/important
• jameshwade/measure
• petersonR/bestNormalize
• spsanderson/healthyR.ai
• spsanderson/healthyR.ts
• jkennel/hydrorecipes

</ Extension packages

• tidymodels/textrecipes
• tidymodels/embed
• tidymodels/themis
• business-science/timetk
• tidymodels/important
• jameshwade/measure
• petersonR/bestNormalize
• spsanderson/healthyR.ai
• spsanderson/healthyR.ts
• jkennel/hydrorecipes

Provides steps to handle text variable

• tokenization
• filtering
• counting

</ Extension packages

• tidymodels/textrecipes
• tidymodels/embed
• tidymodels/themis
• business-science/timetk
• tidymodels/important
• jameshwade/measure
• petersonR/bestNormalize
• spsanderson/healthyR.ai
• spsanderson/healthyR.ts
• jkennel/hydrorecipes

Advanced methods to embed categorical and numeric variables to smaller vector spaces

• weight of evidence
• string collapsing
• PCA variants

</ Extension packages

• tidymodels/textrecipes
• tidymodels/embed
• tidymodels/themis
• business-science/timetk
• tidymodels/important
• jameshwade/measure
• petersonR/bestNormalize
• spsanderson/healthyR.ai
• spsanderson/healthyR.ts
• jkennel/hydrorecipes

Steps to deal with imbalanced data

• up and down-sampling
• SMOTE variants
• ADASYN

</ Extension packages

• tidymodels/textrecipes
• tidymodels/embed
• tidymodels/themis
• business-science/timetk
• tidymodels/important
• jameshwade/measure
• petersonR/bestNormalize
• spsanderson/healthyR.ai
• spsanderson/healthyR.ts
• jkennel/hydrorecipes

Time based methods

• time series signatures
• lags & diffs
• smoothing

</ Thank You! />

read more at recipes.tidymodels