This problem is similar to an already closed issue(#107) but with mlp using nnet.
library(tidymodels)
#> -- Attaching packages ------------------------------------------------- tidymodels 0.0.2 --
#> v broom 0.5.1 v purrr 0.3.2
#> v dials 0.0.2 v recipes 0.1.5
#> v dplyr 0.8.0.1 v rsample 0.0.4
#> v ggplot2 3.1.0 v tibble 2.1.1
#> v infer 0.4.0 v yardstick 0.0.3
#> v parsnip 0.0.2
#> -- Conflicts ---------------------------------------------------- tidymodels_conflicts() --
#> x purrr::discard() masks scales::discard()
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> x recipes::step() masks stats::step()
data(credit_data)
set.seed(7075)
data_split <- initial_split(credit_data, strata = "Status", p = 0.75)
credit_train <- training(data_split)
credit_test <- testing(data_split)
credit_rec <-
recipe(Status ~ ., data = credit_train) %>%
step_knnimpute(Home, Job, Marital, Income, Assets, Debt) %>%
step_dummy(all_nominal(), -Status) %>%
step_center(all_predictors()) %>%
step_scale(all_predictors()) %>%
prep(training = credit_train, retain = TRUE)
test_normalized <- bake(credit_rec, new_data = credit_test, all_predictors())
set.seed(57974)
nnet_fit <-set_engine(mlp("classification",hidden_units =10),"nnet") %>%
fit(Status ~ ., data = juice(credit_rec))
glm_fit <- set_engine(logistic_reg(),"glm") %>%
fit(Status ~ ., data = juice(credit_rec))
#Issue with predict on nnet
glimpse(predict(nnet_fit, new_data = test_normalized, type = "prob"))
#> Observations: 1,113
#> Variables: 2
#> $ .pred_.pred_bad <dbl> 0.5608545, 0.7023505, 0.3303682, 0.4221877, 0...
#> $ .pred_.pred_good <dbl> 0.4391455, 0.2976495, 0.6696318, 0.5778123, 0...
#Normal with predict on glm (No issue)
glimpse(predict(glm_fit, new_data = test_normalized, type = "prob"))
#> Observations: 1,113
#> Variables: 2
#> $ .pred_bad <dbl> 0.04675355, 0.94317298, 0.24316454, 0.06970005, 0.0...
#> $ .pred_good <dbl> 0.95324645, 0.05682702, 0.75683546, 0.93029995, 0.9...
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