BaselearnerCategoricalRidge: One-hot encoded base learner for a categorical feature
In schalkdaniel/compboost: Efficient Component-Wise Boosting Implementation
BaselearnerCategoricalRidge R Documentation
One-hot encoded base learner for a categorical feature
Description
This base learner can be used to estimate effects of categorical
features. The classes are included similar as in the linear model by
using a one-hot encoded data matrix. Additionally, a Ridge penalty
allows unbiased feature selection.
Arguments
data_source
CategoricalDataRaw
Data container of the raw categorical feature.
blearner_type
(character(1))
Type of the base learner (if not specified, blearner_type = "ridge" is used).
The unique id of the base learner is defined by appending blearner_type to
the feature name: paste0(data_source$getIdentifier(), "_", blearner_type).
df
(numeric(1))
Degrees of freedom of the base learner(s).
Format
S4 object.
Usage
BaselearnerCategoricalRidge$new(data_source, list(df))
BaselearnerCategoricalRidge$new(data_source, blearner_type, list(df))
Fields
This class doesn't contain public fields.
Methods
-
$summarizeFactory(): () -> ()
-
$transfromData(newdata): list(InMemoryData) -> matrix()
-
$getMeta(): () -> list()
Inherited methods from Baselearner
-
$getData(): () -> matrix()
-
$getDF(): () -> integer()
-
$getPenalty(): () -> numeric()
-
$getPenaltyMat(): () -> matrix()
-
$getFeatureName(): () -> character()
-
$getModelName(): () -> character()
-
$getBaselearnerId(): () -> character()
Examples
# Sample data:
x = sample(c("one","two"), 20, TRUE)
y = c(one = 0.8, two = -1.2)[x] + rnorm(20, 0, 0.2)
dat = data.frame(x, y)
# S4 API:
ds = CategoricalDataRaw$new(x, "cat")
bl = BaselearnerCategoricalRidge$new(ds, list(df = 1))
bl$getData()
bl$summarizeFactory()
bl$getData()
bl$summarizeFactory()
bl$transformData(list(ds))
bl$getBaselearnerId()
# R6 API:
cboost = Compboost$new(dat, "y")
cboost$addBaselearner("x", "binary", BaselearnerCategoricalRidge)
cboost$train(100, 0)
table(cboost$getSelectedBaselearner())
plotPEUni(cboost, "x", individual = FALSE)
schalkdaniel/compboost documentation built on April 15, 2023, 9:03 p.m.
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| BaselearnerCategoricalRidge | R Documentation |
One-hot encoded base learner for a categorical feature
Description
This base learner can be used to estimate effects of categorical features. The classes are included similar as in the linear model by using a one-hot encoded data matrix. Additionally, a Ridge penalty allows unbiased feature selection.
Arguments
data_source |
CategoricalDataRaw |
blearner_type |
( |
df |
( |
Format
S4 object.
Usage
BaselearnerCategoricalRidge$new(data_source, list(df)) BaselearnerCategoricalRidge$new(data_source, blearner_type, list(df))
Fields
This class doesn't contain public fields.
Methods
-
$summarizeFactory():() -> () -
$transfromData(newdata):list(InMemoryData) -> matrix() -
$getMeta():() -> list()
Inherited methods from Baselearner
-
$getData():() -> matrix() -
$getDF():() -> integer() -
$getPenalty():() -> numeric() -
$getPenaltyMat():() -> matrix() -
$getFeatureName():() -> character() -
$getModelName():() -> character() -
$getBaselearnerId():() -> character()
Examples
# Sample data:
x = sample(c("one","two"), 20, TRUE)
y = c(one = 0.8, two = -1.2)[x] + rnorm(20, 0, 0.2)
dat = data.frame(x, y)
# S4 API:
ds = CategoricalDataRaw$new(x, "cat")
bl = BaselearnerCategoricalRidge$new(ds, list(df = 1))
bl$getData()
bl$summarizeFactory()
bl$getData()
bl$summarizeFactory()
bl$transformData(list(ds))
bl$getBaselearnerId()
# R6 API:
cboost = Compboost$new(dat, "y")
cboost$addBaselearner("x", "binary", BaselearnerCategoricalRidge)
cboost$train(100, 0)
table(cboost$getSelectedBaselearner())
plotPEUni(cboost, "x", individual = FALSE)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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