nullmodel models

In tidypredict: Run Predictions Inside the Database

library(tidypredict)
library(dplyr)
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

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

parsnip::nullmodel() fits a model that ignores the predictors entirely. For regression it predicts the mean of the outcome, and for classification it predicts the observed class frequencies. Predictions are therefore constants, and tidypredict_fit() returns a plain number for regression and a named list of one constant per class for classification. Since the classification output is a list, tidypredict_to_column() and tidypredict_test() are only supported for regression.

tidypredict_ functions

model <- parsnip::nullmodel(mtcars[-1], mtcars$mpg)

• Create the R formula r tidypredict_fit(model)

• Add the predictions to the original table r mtcars %>% tidypredict_to_column(model) %>% glimpse()

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

For classification, one expression per class is returned:

c_model <- parsnip::nullmodel(iris[-5], iris$Species)

tidypredict_fit(c_model)

parsnip

parsnip fitted models are also supported by tidypredict:

library(parsnip)

p_model <- null_model(mode = "regression") %>%
  set_engine("parsnip") %>%
  fit(mpg ~ ., data = mtcars)

tidypredict_fit(p_model)

Parse model spec

Here is an example of the model spec:

pm <- parse_model(model)
str(pm, 2)

tidypredict documentation built on Oct. 18, 2026, 5:06 p.m.