predict_model: Generate predictions for input data with specified model
In shapr: Prediction Explanation with Dependence-Aware Shapley Values

predict_model

R Documentation

Generate predictions for input data with specified model

Description

Performs prediction of response stats::lm(), stats::glm(), ranger::ranger(), mgcv::gam(), workflows::workflow() (i.e., tidymodels models), and xgboost::xgb.train() with binary or continuous response. See details for more information.

Usage

predict_model(x, newdata, ...)

## Default S3 method:
predict_model(x, newdata, ...)

## S3 method for class 'ar'
predict_model(x, newdata, newreg, horizon, ...)

## S3 method for class 'Arima'
predict_model(
  x,
  newdata,
  newreg,
  horizon,
  explain_idx,
  explain_lags,
  y,
  xreg,
  ...
)

## S3 method for class 'forecast_ARIMA'
predict_model(x, newdata, newreg, horizon, ...)

## S3 method for class 'glm'
predict_model(x, newdata, ...)

## S3 method for class 'lm'
predict_model(x, newdata, ...)

## S3 method for class 'gam'
predict_model(x, newdata, ...)

## S3 method for class 'ranger'
predict_model(x, newdata, ...)

## S3 method for class 'workflow'
predict_model(x, newdata, ...)

## S3 method for class 'xgb.Booster'
predict_model(x, newdata, ...)

Arguments

`x`	Model object for the model to be explained.
`newdata`	A data.frame/data.table with the features to predict from.
`...`	`newreg` and `horizon` parameters used in models passed to `⁠[explain_forecast()]⁠`
`horizon`	Numeric. The forecast horizon to explain. Passed to the `predict_model` function.
`explain_idx`	Numeric vector. The row indices in data and reg denoting points in time to explain.
`y`	Matrix, data.frame/data.table or a numeric vector. Contains the endogenous variables used to estimate the (conditional) distributions needed to properly estimate the conditional expectations in the Shapley formula including the observations to be explained.
`xreg`	Matrix, data.frame/data.table or a numeric vector. Contains the exogenous variables used to estimate the (conditional) distributions needed to properly estimate the conditional expectations in the Shapley formula including the observations to be explained. As exogenous variables are used contemporaneously when producing a forecast, this item should contain nrow(y) + horizon rows.

Details

The following models are currently supported:

stats::lm()
stats::glm()
ranger::ranger()
mgcv::gam()
workflows::workflow()
xgboost::xgb.train()

If you have a binary classification model we'll always return the probability prediction for a single class.

If you are explaining a model not supported natively, you need to create the ⁠[predict_model()]⁠ function yourself, and pass it on to as an argument to ⁠[explain()]⁠.

For more details on how to explain such non-supported models (i.e. custom models), see the Advanced usage section of the general usage:
From R: vignette("general_usage", package = "shapr")
Web: https://norskregnesentral.github.io/shapr/articles/general_usage.html#explain-custom-models

Value

Numeric. Vector of size equal to the number of rows in newdata.

Author(s)

Martin Jullum

Examples

# Load example data
data("airquality")
airquality <- airquality[complete.cases(airquality), ]
# Split data into test- and training data
x_train <- head(airquality, -3)
x_explain <- tail(airquality, 3)
# Fit a linear model
model <- lm(Ozone ~ Solar.R + Wind + Temp + Month, data = x_train)

# Predicting for a model with a standardized format
predict_model(x = model, newdata = x_explain)

shapr documentation built on April 4, 2025, 12:18 a.m.