I am currently trying to make a few different models in caret
, ranging from logistic model to XGBoost. Creating the models is easy enough, however when I want to use the models to make predictions on a test set I have set aside before beginning, I get an error messages saying things like:
Error in UseMethod("predict") :
no applicable method for 'predict' applied to an object of class "data.frame"
and:
Error in predict(logistic_model$finalModel, new_data = pd_test)$.pred_class :
$ operator is invalid for atomic vectors`
Here is the logistic model:
set.seed(100)
train_test_split <- initial_split(pd_data, prop = 0.8)
pd_train <- training(train_test_split)
pd_test <- testing(train_test_split)
# caret
# logistic model
# model creation and VIF
log_control <- trainControl(method = "cv", number = 5, classProbs = TRUE,
summaryFunction = twoClassSummary)
logistic_model <- train(default ~ profit_margin + interest_coverage_ratio +
age_of_company + liquidity_ratio_2
+ unpaid_debt_collection
+ adverse_audit_opinion + amount_unpaid_debt
+ payment_reminders, data = pd_train,
trControl = log_control,
method = "glm", family = "binomial", metric = "ROC")
vif(logistic_model$finalModel)
log_class_predictions <- predict(logistic_model$finalModel, new_data = pd_test)$.pred_class
log_predictions <- predict(logistic_model$finalModel$tuneValue,
new_data = pd_test, type = "prob")$.pred_1
How can I fix this so that I can test my models on the untouched test set? I have tried several logistic_model$
choices, but to no avail