Cubist models

In tidypredict: Run Predictions Inside the Database

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
source("_threads.R")
library(tidypredict)
library(Cubist)
library(dplyr)

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

tidypredict_ functions

library(Cubist)
data("BostonHousing", package = "mlbench")

model <- Cubist::cubist(
  x = BostonHousing[, -14],
  y = BostonHousing$medv,
  committees = 3
)

• Create the R formula r tidypredict_fit(model)

• SQL output example r tidypredict_sql(model, dbplyr::simulate_odbc())

• Add the prediction to the original table ```r library(dplyr)

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

We are not able to give an exact match of the original predictions due to a minor bug in Cubist.

Parse model spec

Here is an example of the model spec:

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

str(pm$terms[1:2])

Limitations

• tidypredict_test() is not supported
• Prediction intervals are not supported
• Cubist uses 32-bit floats internally, which may cause prediction discrepancies at exact split boundaries. See the float precision article for details.

tidypredict documentation built on Aug. 24, 2026, 9:10 a.m.