# factor encoded predictor Code new_values_ch <- bake(class_test, new_data = new_dat_ch, contains("embed")) Condition Warning: ! There was 1 column that was a factor when the recipe was prepped: * `x3` i This may cause errors when processing new data. --- Code new_values_ch <- bake(class_test, new_data = new_dat_ch, contains("embed")) Condition Warning: ! There was 1 column that was a factor when the recipe was prepped: * `x3` i This may cause errors when processing new data. # bad args Code recipe(Species ~ ., data = three_class) %>% step_embed(Sepal.Length, outcome = vars( Species)) %>% prep(training = three_class, retain = TRUE) Condition Error in `step_embed()`: Caused by error in `prep()`: x All columns selected for the step should be string, factor, or ordered. * 1 double variable found: `Sepal.Length` # check_name() is used Code prep(rec, training = dat) Condition Error in `step_embed()`: Caused by error in `bake()`: ! Name collision occurred. The following variable names already exist: * `x3_embed_1` # empty printing Code rec Message -- Recipe ---------------------------------------------------------------------- -- Inputs Number of variables by role outcome: 1 predictor: 10 -- Operations * Embedding of factors via tensorflow for: --- Code rec Message -- Recipe ---------------------------------------------------------------------- -- Inputs Number of variables by role outcome: 1 predictor: 10 -- Training information Training data contained 32 data points and no incomplete rows. -- Operations * Embedding of factors via tensorflow for: | Trained # keep_original_cols - can prep recipes with it missing Code rec <- prep(rec) Condition Warning: `keep_original_cols` was added to `step_embed()` after this recipe was created. i Regenerate your recipe to avoid this warning. # printing Code print(rec) Message -- Recipe ---------------------------------------------------------------------- -- Inputs Number of variables by role outcome: 1 predictor: 3 -- Operations * Embedding of factors via tensorflow for: x3 --- Code prep(rec) Message -- Recipe ---------------------------------------------------------------------- -- Inputs Number of variables by role outcome: 1 predictor: 3 -- Training information Training data contained 500 data points and no incomplete rows. -- Operations * Embedding of factors via tensorflow for: x3 | Trained