Static AuditorFitter based on Subgroups

Used to assess multi-calibration based on a list of binary subgroup_masks passed during initialization.

Value

AuditorFitter

list with items

• corr: pseudo-correlation between residuals and learner prediction.

• l: the trained learner.

See also

Other AuditorFitter: CVLearnerAuditorFitter, LearnerAuditorFitter, SubpopAuditorFitter

Super class

mcboost::AuditorFitter -> SubgroupAuditorFitter

Public fields

subgroup_masks

list
List of subgroup masks. Initialize a SubgroupAuditorFitter

Methods

Public methods

• SubgroupAuditorFitter$new()

• SubgroupAuditorFitter$fit()

• SubgroupAuditorFitter$clone()

Inherited methods

• mcboost::AuditorFitter$fit_to_resid()

---

Method new()

Initializes a SubgroupAuditorFitter that assesses multi-calibration within each group defined by the `subpops'.

Usage

SubgroupAuditorFitter$new(subgroup_masks)

Arguments

subgroup_masks

list
List of subgroup masks. Subgroup masks are list(s) of integer masks, each with the same length as data to be fitted on. They allow defining subgroups of the data.

---

Method fit()

Fit the learner and compute correlation

Usage

SubgroupAuditorFitter$fit(data, resid, mask)

Arguments

data

data.table
Features.

resid

numeric
Residuals (of same length as data).

mask

integer
Mask applied to the data. Only used for SubgroupAuditorFitter.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

SubgroupAuditorFitter$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

 library("data.table")
 data = data.table(
   "AGE_0_10" =  c(1, 1, 0, 0, 0),
   "AGE_11_20" = c(0, 0, 1, 0, 0),
   "AGE_21_31" = c(0, 0, 0, 1, 1),
   "X1" = runif(5),
   "X2" = runif(5)
 )
 label = c(1,0,0,1,1)
 masks = list(
   "M1" = c(1L, 0L, 1L, 1L, 0L),
   "M2" = c(1L, 0L, 0L, 0L, 1L)
 )
 sg = SubgroupAuditorFitter$new(masks)