完整建模流程

在 R 中使用 tidymodels 建立模型

David Svancer

Data Scientist

資料重抽樣

建立訓練與測試資料集

initial_split()
- 建立資料切分物件
training()
- 建立訓練資料集
testing()
- 建立測試資料集

leads_split <- initial_split(leads_df, 
                             strata = purchased)


leads_training <- leads_split %>% 
  training()


leads_test <- leads_split %>% 
  testing()

模型規格

用 parsnip 指定模型

logistic_reg()
- 邏輯斯迴歸的通用介面
set_engine()
- 使用「glm」引擎
set_mode()
- purchased 為名目型輸出變數
- 模式應為「classification」

logistic_model <- logistic_reg() %>%

  set_engine('glm') %>%

  set_mode('classification')

Logistic Regression Model 
Specification (classification)

Computational engine: glm

特徵工程

用 recipes 指定特徵工程步驟

recipe()
- 模型公式與訓練資料
step_*() 函式
- 逐步前處理流程

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()

配方訓練（Recipe）

在訓練資料上訓練特徵工程步驟

prep()
- 將 recipe 物件傳入 prep()
- 加入 leads_training 作為訓練資料

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]

前處理訓練資料

將已訓練的 recipe 套用到訓練資料，並儲存結果供模型擬合

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

前處理測試資料

將已訓練的 recipe 套用到測試資料，並儲存結果供模型評估

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

模型擬合與預測

用 fit() 訓練邏輯斯迴歸模型

使用「前處理後的訓練資料集」leads_training_prep

使用 predict() 取得模型預測

預測結果類別與機率
使用「前處理後的測試資料集」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')

合併預測結果

將預測合併為 yardstick 評估可用的結果資料集

從測試資料集中選出實際輸出變數 purchased
用 bind_cols() 合併預測欄位

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

模型評估

用 yardstick 評估模型效能

結果資料可搭配所有 yardstick 指標函式評估模型
混淆矩陣、靈敏度、特異度等指標

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

          Truth
Prediction yes  no
       yes  77  34
       no   43 178

一起來練習吧！

在 R 中使用 tidymodels 建立模型