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R로 배우는 Feature Engineering

Jorge Zazueta

Research Professor. Head of the Modeling Group at the School of Economics, UASLP

모델링 플로의 개요도

전형적인 고수준 모델링 단계입니다.

모델링 과정의 순서도.

모델링 플로의 개요도

전형적인 고수준 모델링 단계입니다.

특성 엔지니어링을 강조한 모델링 워크플로.

준비

기본 정리와 데이터 분할을 먼저 수행합니다.

loans <- # Basic housekeeping
  loans %>%
  mutate(across(where(is_character),
                  as_factor)) %>%
  mutate(across(Credit_History,
                  as_factor))

set.seed(123) # Set up splits
split <- initial_split(loans, 
        strata = Loan_Status)
test <- testing(split)
train <- training(split)

glimpse(train)

Rows: 460
Columns: 13
$ Loan_ID           <fct> LP001003...
$ Gender            <fct> Male, Ma...
$ Married           <fct> Yes, No,...
$ Dependents        <fct> 1, 0, 0,...
$ Education         <fct> Graduate...
$ Self_Employed     <fct> No, No, ...
$ ApplicantIncome   <dbl> 4583, 18...
$ CoapplicantIncome <dbl> 1508, 28...
$ LoanAmount        <dbl> 128, 114...
$ Loan_Amount_Term  <dbl> 360, 360...
$ Credit_History    <fct> 1, 1, 0,...
$ Property_Area     <fct> Rural, R...
$ Loan_Status       <fct> N, N, N,...

전처리

레시피는 간단할 수도, 매우 복잡할 수도 있습니다.

recipe <- recipe(Loan_Status ~ .,
data = train) %>%
  update_role(Loan_ID, 
  new_role = "ID") %>%
  step_normalize(all_numeric_predictors()) %>% 
  step_impute_knn(all_predictors()) %>%
  step_dummy(all_nominal_predictors())

recipe

Recipe

Inputs:

      role #variables
        ID          1
   outcome          1
 predictor         11

Operations:

Centering and scaling for all_numeric_predictors()
K-nearest neighbor imputation for all_predictors()
Dummy variables from all_nominal_predictors()

모델

워크플로 설정

lr_model <- logistic_reg() %>%
  set_engine("glmnet") %>%
  set_args(mixture = 1, penalty = tune())

lr_penalty_grid <- grid_regular(
  penalty(range = c(-3, 1)),
  levels = 30)

lr_workflow <-
  workflow() %>%
  add_model(lr_model) %>%
  add_recipe(recipe)

lr_workflow

--Workflow -------------------------------
Preprocessor: Recipe
Model: logistic_reg()

-- Preprocessor --------------------------
3 Recipe Steps
- step_normalize()
- step_impute_knn()
- step_dummy()

-- Model ---------------------------------
로지스틱 회귀 모델 사양(분류)

주요 인자:
  penalty = tune()
  mixture = 1
계산 엔진: glmnet

평가

Lasso 패널티 튜닝

lr_tune_output <- tune_grid( 
  lr_workflow,
  resamples = vfold_cv(train, v = 5),
  metrics = metric_set(roc_auc),
  grid = penalty_grid)

autoplot(tune_output)

ROC_AUC vs. 정규화

정규화에 따른 ROC_AUC 차트.

평가

최종 모델 적합

best_penalty <-
select_by_one_std_err(lr_tune_output,
metric = 'roc_auc', desc(penalty)) 

lr_final_fit<-
finalize_workflow(lr_workflow, best_penalty) %>%
  fit(data = train)

lr_final_fit %>%
  augment(test) %>% 
  class_evaluate(truth = Loan_Status,
              estimate = .pred_class,
                         .pred_Y)

성능 지표

# A tibble: 2 × 3
  .metric  .estimator .estimate
  <chr>    <chr>          <dbl>
1 accuracy binary         0.818
2 roc_auc  binary         0.813

연습해 봅시다!

R로 배우는 Feature Engineering