[英]tidymodels metric_set:Error: All inputs to `metric_set()` must be functions. These inputs are not: (2)
我在tidymodels
包中使用了recipe()
函數來填補缺失值和修復不平衡的數據。
這是我的數據;
mer_df <- mer2 %>%
filter(!is.na(laststagestatus2)) %>%
select(Id, Age_Range__c, Gender__c, numberoflead, leadduration, firsttouch, lasttouch, laststagestatus2)%>%
mutate_if(is.character, factor) %>%
mutate_if(is.logical, as.integer)
# A tibble: 197,836 x 8
Id Age_Range__c Gender__c numberoflead leadduration firsttouch lasttouch
<fct> <fct> <fct> <int> <dbl> <fct> <fct>
1 0010~ NA NA 2 5.99 Dealer IB~ Walk in
2 0010~ NA NA 1 0 Online Se~ Online S~
3 0010~ NA NA 1 0 Walk in Walk in
4 0010~ NA NA 1 0 Online Se~ Online S~
5 0010~ NA NA 2 0.0128 Dealer IB~ Dealer I~
6 0010~ NA NA 1 0 OB Call OB Call
7 0010~ NA NA 1 0 Dealer IB~ Dealer I~
8 0010~ NA NA 4 73.9 Dealer IB~ Walk in
9 0010~ NA Male 24 0.000208 OB Call OB Call
10 0010~ NA NA 18 0.000150 OB Call OB Call
# ... with 197,826 more rows, and 1 more variable: laststagestatus2 <fct>
這是我的代碼;
mer_rec <- recipe(laststagestatus2 ~ ., data = mer_train)%>%
step_medianimpute(numberoflead,leadduration)%>%
step_knnimpute(Gender__c,Age_Range__c,fisrsttouch,lasttouch) %>%
step_other(Id,firsttouch) %>%
step_other(Id,lasttouch) %>%
step_dummy(all_nominal(), -laststagestatus2) %>%
step_smote(laststagestatus2)
mer_rec %>% prep() %>% juice()
glm_spec <- logistic_reg() %>%
set_engine("glm")
rf_spec <- rand_forest(trees = 1000) %>%
set_mode("classification") %>%
set_engine("ranger")
mer_wf <- workflow() %>%
add_recipe(mer_rec)
它一直工作到這里現在我使用metric_set()
函數來適應每個重采樣。
這是我的代碼如下:
mer_metrics <- metric_set(roc_auc, accuracy, sensitivity, specificity)
glm_rs <- mer_wf %>%
add_model(glm_spec) %>%
fit_resamples(
resamples = mer_folds,
metrics = mer_metrics,
control = control_resamples(save_pred = TRUE)
我收到錯誤說:
Error: All inputs to `metric_set()` must be functions. These inputs are not: (2).
但它在沒有精度參數的情況下工作
merco_metrics <- metric_set(roc_auc, sensitivity, specificity)
有人對如何做到這一點有任何建議嗎? 非常感謝您的幫助!
可能在您的環境中定義了另一個名為accuracy
變量。 嘗試輸入yardstick::accuracy
。
mer_metrics <- metric_set(roc_auc, yardstick::accuracy, 靈敏度, 特異性)
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