Static AuditorFitter based on Subpopulations

Used to assess multi-calibration based on a list of binary valued columns: subpops 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, SubgroupAuditorFitter

Super class

mcboost::AuditorFitter -> SubpopAuditorFitter

Public fields

subpops

list
List of subpopulation indicators. Initialize a SubpopAuditorFitter

Methods

Public methods

• SubpopAuditorFitter$new()

• SubpopAuditorFitter$fit()

• SubpopAuditorFitter$clone()

Inherited methods

• mcboost::AuditorFitter$fit_to_resid()

---

Method new()

Initializes a SubpopAuditorFitter that assesses multi-calibration within each group defined by the subpops'. Names in subpops` must correspond to columns in the data.

Usage

SubpopAuditorFitter$new(subpops)

Arguments

subpops

list
Specifies a collection of characteristic attributes and the values they take to define subpopulations e.g. list(age = c('20-29','30-39','40+'), nJobs = c(0,1,2,'3+'), ,..).

---

Method fit()

Fit the learner and compute correlation

Usage

SubpopAuditorFitter$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

SubpopAuditorFitter$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

  library("data.table")
  data = data.table(
    "AGE_NA" = c(0, 0, 0, 0, 0),
    "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)
  pops = list("AGE_NA", "AGE_0_10", "AGE_11_20", "AGE_21_31", function(x) {x[["X1" > 0.5]]})
  sf = SubpopAuditorFitter$new(subpops = pops)
  sf$fit(data, label - 0.5)
#> [[1]]
#> [1] 0.2
#> 
#> [[2]]
#> <SubpopPredictor>
#>   Inherits from: <Predictor>
#>   Public:
#>     clone: function (deep = FALSE) 
#>     fit: function (data, labels) 
#>     initialize: function (subpop, value) 
#>     predict: function (data, ...) 
#>     subpop: function (rw) 
#>     value: 0.2
#>