nnet models
In tidypredict: Run Predictions Inside the Database

if (requireNamespace("nnet", quietly = TRUE)) {
  library(tidypredict)
  library(nnet)
  library(dplyr)
  eval_code <- TRUE
} else {
  eval_code <- FALSE
}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = eval_code
)

| Function |Works| |---------------------------------------------------------------|-----| |tidypredict_fit(), tidypredict_sql(), parse_model() | ✔ | |tidypredict_to_column() | ✔ | |tidypredict_test() | ✔ | |tidypredict_interval(), tidypredict_sql_interval() | ✗ | |parsnip | ✔ |

nnet::nnet() fits feed-forward neural networks with a single hidden layer. Each hidden unit is the weighted sum of the predictors passed through the logistic squashing function, and the output units are the weighted sums of the hidden units, left unsquashed when the network was fit with linout = TRUE.

Classification models predict one probability per outcome class, so tidypredict_fit() returns a named list of expressions rather than a single expression. For those models tidypredict_to_column() and tidypredict_test() are not supported.

`tidypredict_` functions

library(nnet)

set.seed(100)
model <- nnet(mpg ~ wt + hp, data = mtcars, size = 3, linout = TRUE, trace = FALSE)

Create the R formula r tidypredict_fit(model)
Add the predictions to the original table ```r library(dplyr)

mtcars %>% tidypredict_to_column(model) %>% glimpse() ```

Confirm that the results match the model's predict() results r tidypredict_test(model, mtcars)

Classification

set.seed(100)
cls_model <- nnet(Species ~ ., data = iris, size = 3, trace = FALSE)

fit <- tidypredict_fit(cls_model)
names(fit)

probs <- sapply(fit, \(f) rlang::eval_tidy(f, iris))
all.equal(unname(probs), unname(predict(cls_model, iris, type = "raw")))

parsnip

parsnip fitted models are also supported by tidypredict:

library(parsnip)

set.seed(100)
p_model <- mlp(mode = "regression", hidden_units = 3, epochs = 100) %>%
  set_engine("nnet") %>%
  fit(mpg ~ wt + hp, data = mtcars)

tidypredict_fit(p_model)

Note that parsnip runs the class probabilities of predict.nnet() through a second softmax, so the expressions returned for a classification mlp() model match predict(model, type = "prob") rather than predict(model$fit, type = "raw").