**Overview**

I have produced four models using the **tidymodels package** with the **data frame FID** (see below):

**General Linear Model****Bagged Tree****Random Forest****Boosted Trees**

The data frame contains **three predictors** :

- Year (numeric)
- Month (Factor)
- Days (numeric)

**The dependent variable is Frequency (numeric)**

**Aim**

My aim is to undertake model predictions to extract the **class** and **probability** values for all **fitted models** , which have all undergone **10 fold cross-validation** .

I am attempting to use the functions **prep(), juice(), and bake()** in order to generate the correct data objects for model predictions objects by following this tutorial below.

**Tutorial (see screenshots below)**

https://meghan.rbind.io/post/tidymodels-intro/

After producing the model prediction values (i.e class and probability) for all four models, the ultimate aim is to produce **confusion matrices** and **receiver operating curves (ROC)** to evaluate all models. Therefore, I need to bind the **true values** from the testing data, with the **class** and **probability** columns extracted from these model predictions.

**Issue**

I am trying to run the predict() function to produce the **class and probability values** from the tutorial (see screenshots below and the link above), but I am experiencing this error message below.

```
**Error Messages**
##Class prediction object
Error in UseMethod("predict") :
no applicable method for 'predict' applied to an object of class "c('tbl_df', 'tbl', 'data.frame')"
##Prob
Error in UseMethod("predict") :
no applicable method for 'predict' applied to an object of class "c('resample_results', 'tune_results', 'tbl_df', 'tbl', 'data.frame')"
```

If anyone is able to help, I would be deeply appreciative

Many thanks in advance.

**Screen-shots from the tutorial**

**R-code**

```
##################################################
##Model Prediction
###################################################
##Open the tidymodels package
library(tidymodels)
library(tidyverse)
library(glmnet)
library(parsnip)
library(rpart)
library(tidyverse) # manipulating data
library(skimr) # data visualization
library(baguette) # bagged trees
library(future) # parallel processing & decrease computation time
library(xgboost) # boosted trees
library(ranger)
library(yardstick)
library(purrr)
library(forcats)
###########################################################
#split this single dataset into two: a training set and a testing set
data_split <- initial_split(FID)
# Create data frames for the two sets:
train_data <- training(data_split)
test_data <- testing(data_split)
# resample the data with 10-fold cross-validation (10-fold by default)
cv <- vfold_cv(train_data, v=10)
###########################################################
##Produce the recipe
rec <- recipe(Frequency ~ ., data = FID) %>%
step_nzv(all_predictors(), freq_cut = 0, unique_cut = 0) %>% # remove variables with zero variances
step_novel(all_nominal()) %>% # prepares test data to handle previously unseen factor levels
step_medianimpute(all_numeric(), -all_outcomes(), -has_role("id vars")) %>% # replaces missing numeric observations with the median
step_dummy(all_nominal(), -has_role("id vars")) # dummy codes categorical variables
###########################################################
##Create Models
###########################################################
##########################################################
##General Linear Models
#########################################################
##glm
mod_glm<-linear_reg(mode="regression",
penalty = 0.1,
mixture = 1) %>%
set_engine("glmnet")
##Create workflow
wflow_glm <- workflow() %>%
add_recipe(rec) %>%
add_model(mod_glm)
##Fit the model
plan(multisession)
fit_glm <- fit_resamples(
wflow_glm,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(save_pred = TRUE,
extract = function(x) extract_model(x)))
##########################################################
##Bagged Trees
##########################################################
#####Bagged Trees
mod_bag <- bag_tree() %>%
set_mode("regression") %>%
set_engine("rpart", times = 10) #10 bootstrap resamples
##Create workflow
wflow_bag <- workflow() %>%
add_recipe(rec) %>%
add_model(mod_bag)
##Fit the model
plan(multisession)
fit_bag <- fit_resamples(
wflow_bag,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(save_pred = TRUE,
extract = function(x) extract_model(x)))
###################################################
##Random forests
###################################################
mod_rf <-rand_forest(trees = 1e3) %>%
set_engine("ranger",
num.threads = parallel::detectCores(),
importance = "permutation",
verbose = TRUE) %>%
set_mode("regression")
##Create Workflow
wflow_rf <- workflow() %>%
add_model(mod_rf) %>%
add_recipe(rec)
##Fit the model
plan(multisession)
fit_rf<-fit_resamples(
wflow_rf,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(save_pred = TRUE,
extract = function(x) extract_model(x)))
############################################################
##Boosted Trees
############################################################
mod_boost <- boost_tree() %>%
set_engine("xgboost", nthreads = parallel::detectCores()) %>%
set_mode("regression")
##Create Workflow
wflow_boost <- workflow() %>%
add_recipe(rec) %>%
add_model(mod_boost)
##Fit model
plan(multisession)
fit_boost <-fit_resamples(
wflow_boost,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(save_pred = TRUE,
extract = function(x) extract_model(x)))
##################################################
##Prep the models for model prediction
##################################################
# Extract our prepped training data
# and "bake" our testing data
prep<-prep(rec)
training_baked<-juice(prep)
testing_baked <- prep %>% bake(test_data)
# Run the model with our training data
# Find the class predictions from our testing data
# And add back in the true values from testing data
predictions_class <- %>% fit_glm %>%
predict(new_data = testing_baked) %>%
bind_cols(testing_baked %>% dplyr::select(Frequency))
##Error message
Error in UseMethod("predict") :
no applicable method for 'predict' applied to an object of class "c('tbl_df', 'tbl', 'data.frame')"
# Find the probability predictions
# And add all together
predictions_Prob <- fit_glm %>%
predict(testing_baked, type = "prob") %>%
bind_cols(predictions_class)
##Error message
Error in UseMethod("predict") :
no applicable method for 'predict' applied to an object of class
"c('resample_results', 'tune_results', 'tbl_df', 'tbl', 'data.frame')"
```

**Data frame - FID**

```
structure(list(Year = c(2015, 2015, 2015, 2015, 2015, 2015, 2015,
2015, 2015, 2015, 2015, 2015, 2016, 2016, 2016, 2016, 2016, 2016,
2016, 2016, 2016, 2016, 2016, 2016, 2017, 2017, 2017, 2017, 2017,
2017, 2017, 2017, 2017, 2017, 2017, 2017), Month = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L), .Label = c("January", "February", "March",
"April", "May", "June", "July", "August", "September", "October",
"November", "December"), class = "factor"), Frequency = c(36,
28, 39, 46, 5, 0, 0, 22, 10, 15, 8, 33, 33, 29, 31, 23, 8, 9,
7, 40, 41, 41, 30, 30, 44, 37, 41, 42, 20, 0, 7, 27, 35, 27,
43, 38), Days = c(31, 28, 31, 30, 6, 0, 0, 29, 15,
29, 29, 31, 31, 29, 30, 30, 7, 0, 7, 30, 30, 31, 30, 27, 31,
28, 30, 30, 21, 0, 7, 26, 29, 27, 29, 29)), row.names = c(NA,
-36L), class = "data.frame")
```