Alur kerja pemodelan lengkap

Pemodelan dengan tidymodels di R

David Svancer

Data Scientist

Penyampelan ulang data

Membuat dataset latih dan uji

initial_split()
- Buat objek pembagi data
training()
- Bangun dataset latih
testing()
- Bangun dataset uji

leads_split <- initial_split(leads_df, 
                             strata = purchased)


leads_training <- leads_split %>% 
  training()


leads_test <- leads_split %>% 
  testing()

Spesifikasi model

Spesifikasikan model dengan parsnip

logistic_reg()
- Antarmuka umum untuk model regresi logistik
set_engine()
- Engine 'glm'
set_mode()
- purchased adalah variabel keluaran nominal
- Mode harus 'classification'

logistic_model <- logistic_reg() %>%

  set_engine('glm') %>%

  set_mode('classification')

Logistic Regression Model 
Specification (classification)

Computational engine: glm

Rekayasa fitur

Tentukan langkah rekayasa fitur dengan recipes

recipe()
- Rumus model dan data latih
Fungsi step_*()
- Langkah prapemrosesan berurutan

leads_recipe <- recipe(purchased ~ .,
                       data = leads_training) %>%

  step_corr(all_numeric(), threshold = 0.9) %>% 
  step_normalize(all_numeric()) %>% 
  step_dummy(all_nominal(), -all_outcomes())

leads_recipe

Data Recipe
Inputs:
      role #variables
   outcome          1
 predictor          6

Operations:
Correlation filter on all_numeric()
Centering and scaling for all_numeric()
Dummy variables from all_nominal(), -all_outcomes()

Pelatihan recipe

Latih langkah rekayasa fitur pada data latih

prep()
- Kirim objek recipe ke prep()
- Tambahkan leads_training sebagai data latih

leads_recipe_prep <- leads_recipe %>% 
  prep(training = leads_training)

leads_recipe_prep

Data Recipe
Inputs:
      role #variables
   outcome          1
 predictor          6
Training data contained 996 data points 
and no missing data.

Operations:
Correlation filter removed pages_per_visit [trained]
Centering and scaling for total_visits ... [trained]
Dummy variables from lead_source, us_location [trained]

Praolah data latih

Terapkan recipe terlatih ke data latih dan simpan hasilnya untuk pemodelan

leads_training_prep <- leads_recipe_prep %>% 
  bake(new_data = NULL)

leads_training_prep

# A tibble: 996 x 11
total_visits  total_time   ... lead_source_email  lead_source_organic_search ...  us_location_west
     <dbl>      <dbl>                <dbl>                      <dbl>                    <dbl>        
 1    0.611      0.958      ...       0                          0            ...         1
 2    0.103     -0.747      ...       1                          0            ...         0
 3    0.611     -0.278      ...       0                          1            ...         1
 4   -0.151     -0.842      ...       0                          0            ...         1
 5   -0.659      1.19       ...       1                          0            ...         0 
# ... with 991 more rows

Praolah data uji

Terapkan recipe terlatih ke data uji dan simpan hasilnya untuk evaluasi model

leads_test_prep <- leads_recipe_prep %>% 
  bake(new_data = leads_test)

leads_test_prep

# A tibble: 332 x 11
 total_visits  total_time  ...  lead_source_email  lead_source_organic_search ...  us_location_west
     <dbl>      <dbl>                <dbl>                      <dbl>                    <dbl>        
 1    0.864     -0.984     ...        0                          0            ...         1
 2   -0.151      1.33      ...        0                          0            ...         0
 3   -0.405     -0.843     ...        0                          1            ...         1
 4   -0.659     -1.14      ...        1                          0            ...         0
 5    1.12       0.725     ...        0                          0            ...         1   
# ... with 327 more rows

Pelatihan model dan prediksi

Latih model regresi logistik dengan fit()

Gunakan dataset latih yang sudah dipraolah, leads_training_prep

Dapatkan prediksi model dengan predict()

Prediksi kelas keluaran dan probabilitasnya
Gunakan dataset uji yang sudah dipraolah, leads_test_prep

logistic_fit <- logistic_model %>% 
  fit(purchased ~ .,
      data = leads_training_prep)

class_preds <- predict(logistic_fit, 
                       new_data = leads_test_prep,
                       type = 'class')

prob_preds <- predict(logistic_fit, 
                      new_data = leads_test_prep,
                      type = 'prob')

Menggabungkan hasil prediksi

Gabungkan prediksi menjadi dataset hasil untuk fungsi metrik yardstick

Pilih variabel keluaran aktual, purchased dari data uji
Gabungkan prediksi dengan bind_cols()

leads_results <- leads_test %>% 
  select(purchased) %>%

  bind_cols(class_preds, prob_preds)


leads_results

# A tibble: 332 x 4
   purchased .pred_class .pred_yes .pred_no
   <fct>     <fct>           <dbl>    <dbl>
 1 no        no             0.257     0.743
 2 yes       yes            0.896     0.104
 3 no        no             0.0852    0.915
 4 no        no             0.183     0.817
 5 yes       yes            0.776     0.224
# ... with 327 more rows

Evaluasi model

Evaluasi kinerja model dengan yardstick

Data hasil dapat dipakai di semua fungsi metrik yardstick untuk evaluasi model
Confusion matrix, sensitivitas, spesifisitas, dan metrik lain

leads_results %>% 
  conf_mat(truth = purchased, 
           estimate = .pred_class)

          Truth
Prediction yes  no
       yes  77  34
       no   43 178

Ayo berlatih!

Pemodelan dengan tidymodels di R