int_conformal_quantile {probably}

Prediction intervals via conformal inference and quantile regression

Description

Nonparametric prediction intervals can be computed for fitted regression workflow objects using the split conformal inference method described by Romano et al (2019). To compute quantiles, this function uses Quantile Random Forests instead of classic quantile regression.

Usage

int_conformal_quantile(object, ...)

## S3 method for class 'workflow'
int_conformal_quantile(object, train_data, cal_data, level = 0.95, ...)

Arguments

object

A fitted workflows::workflow() object.

...

Options to pass to quantregForest::quantregForest() (such as the number of trees).

train_data, cal_data

Data frames with the predictor and outcome data. train_data should be the same data used to produce object and cal_data is used to produce predictions (and residuals). If the workflow used a recipe, these should be the data that were inputs to the recipe (and not the product of a recipe).

level

The confidence level for the intervals.

Details

Note that the significance level should be specified in this function (instead of the predict() method).

cal_data should be large enough to get a good estimates of a extreme quantile (e.g., the 95th for 95% interval) and should not include rows that were in the original training set.

Note that the because of the method used to construct the interval, it is possible that the prediction intervals will not include the predicted value.

Value

An object of class "int_conformal_quantile" containing the information to create intervals (which includes object). The predict() method is used to produce the intervals.

References

Romano, Yaniv, Evan Patterson, and Emmanuel Candes. "Conformalized quantile regression." Advances in neural information processing systems 32 (2019).

See Also

predict.int_conformal_quantile()

Examples

library(workflows)
library(dplyr)
library(parsnip)
library(rsample)
library(tune)
library(modeldata)

set.seed(2)
sim_train <- sim_regression(500)
sim_cal <- sim_regression(200)
sim_new <- sim_regression(5) %>% select(-outcome)

# We'll use a neural network model
mlp_spec <-
  mlp(hidden_units = 5, penalty = 0.01) %>%
  set_mode("regression")

mlp_wflow <-
  workflow() %>%
  add_model(mlp_spec) %>%
  add_formula(outcome ~ .)

mlp_fit <- fit(mlp_wflow, data = sim_train)

mlp_int <- int_conformal_quantile(mlp_fit, sim_train, sim_cal,
  level = 0.90
)
mlp_int

predict(mlp_int, sim_new)