`# Create a split object
train_test_split <-
rsample::initial_split(
data = nomissingprep,
prop = 0.80
)
# Split the data and build a training and testing data set
train_test_split <- rsample::initial_split(data = nomissingprep,prop = 0.80)
train.data <- train_test_split %>% training()
test.data <- train_test_split %>% testing()
## Recipe Creation
rec <- recipe(preprecentyear ~ ., data = train.data)
## Validation Set
cv_folds <-
vfold_cv(train.data,
v = 5,
strata = preprecentyear)
## Model Fitting -- Random Forest
library(ranger)
rf_spec <-
rand_forest() %>%
set_engine("ranger", importance = "impurity") %>%
set_mode("classification")
## Workflow --Random Forest
rf_wflow <-
workflow() %>%
add_recipe(rec) %>%
add_model(rf_spec)
##Random Forest Metrics
rf_res <-
rf_wflow %>%
fit_resamples(
resamples = cv_folds,
metrics = metric_set(
recall, precision, f_meas,
accuracy, kap,
roc_auc, sens, spec),
control = control_resamples(save_pred = TRUE)
)
`
validate_function_class()中的错误：！度量函数的组合必须是：
- 仅限数字度量
- 类度量和类概率度量的混合
以下度量函数类型正在混合使用：
- 其他(调用名称空间：插入符号，精度名称空间：插符号，规范名称空间：readr(
- 等级(f_meas、精度、kap、sens(
- prob(roc_auc(
我不确定如何修复此错误。随机森林度量之前的所有其他代码都很适合。任何建议都非常受欢迎。感谢
log_res <-
tidymodels::tidymodels_prefer( log_wflow %>%
fit_resamples(
resamples = cross_validation,
metrics = metric_set(
kap, sensitivity, specificity, precision_vec, recall_vec),
control = control_resamples(
save_pred = TRUE)
)
)
log_res
试试这个方法。