Predicted vs Observed Calibration Plot — pred_vs

Creates a Hosmer-style calibration chart showing average predicted values against average observed values across bins of the prediction space. A yellow exposure bar on the secondary axis shows the distribution of data.

Usage

pred_vs_obs(data, ...)

# Default S3 method
pred_vs_obs(
  data,
  pred = "predict",
  obs = "observed",
  exposure = "exposure",
  bins = 10L,
  type_agg = c("equal_exposure", "equal_range"),
  title = "",
  ret = c("plot", "data"),
  ...
)

# S3 method for class 'modelblueprint'
pred_vs_obs(
  data,
  set = c("train", "test", "holdout"),
  bins = 10L,
  type_agg = c("equal_exposure", "equal_range"),
  title = NULL,
  ret = c("plot", "data"),
  ...,
  precomputed_preds = NULL
)

Arguments

data: A modelblueprint object.
...: Passed to pred_vs_obs.default().
pred: [character(1)] Name of the predictions column.
obs: [character(1)] Name of the observed target column.
exposure: [character(1)] Name of the exposure column. If the column is absent, every row is given weight 1. Default "exposure".
bins: [integer(1)] Number of bins. Default 10L.
type_agg: [character(1)] "equal_exposure" or "equal_range".
title: [character(1)] Chart title. Defaults to model_display_name (with the set name appended when plotting multiple sets).
ret: [character(1)] "plot" or "data". Default "plot".
set: [character] Dataset splits to use: any of "train", "test", "holdout". Defaults to all available (non-NULL) sets. When more than one set is used, a named list with one result per set is returned.
precomputed_preds: [numeric | NULL] Optional vector of pre-computed predictions (one per row of the requested set). When supplied, the internal predict.modelblueprint() call is skipped.

Value

A plotly object or data.table depending on ret.

Examples

# \donttest{
mb <- modelblueprint(
  model  = glm(vs ~ wt + hp, data = mtcars, family = binomial),
  train  = mtcars,
  y_name = "vs",
  model_display_name = "logistic_vs"
)
pred_vs_obs(mb)

# }