bandit_ucb-class | R Documentation |
An UCB bandit reference class (RC) object.
bandit_ucb(formula, data, family = c("gaussian", "binomial"),
alpha = 1, contrasts = NULL, newLevels = FALSE,
db = NULL, path = NULL)
formula |
an object of class "formula" (or one that can be coerced
to that class): a symbolic description of the model that is fitted. The response
must be named |
data |
a data frame (or object coercible by |
family |
a character string describing the error distribtion and link function
to be used in the model. Can be either |
alpha |
the LinUCB tuning parameter. A positive scalar. Higher values of
|
contrasts |
an optional list. See the |
newLevels |
a logical value indicating whether to allow for new factor levels when adding samples. Default is FALSE. |
db |
an optional named list of arguments passed to |
path |
an optional character string naming a folder open for writing. |
The RC class "bandit_ucb"
inherits from class "bandit"
.
The introductory vignette provides a detailed explanation of LinUCB algorithms, and
their implementation with banditr
. See the Examples section.
alpha
the linear UCB tuning parameter. See details.
family
supported response type. See banditGlmnet
for details.
train(parRidge = NULL, parLasso = NULL, seed = NULL)
trains the model using
banditGlmnet
and all completed experiments.
parRidge
and parLasso
are optional lists of
parameters passed to banditGlmnet
to control the fitting process.
seed
is an optional seeding value for the random number generator.
tune(param = 'lambdaRidge', value = 'auto', lambdaAuto = 'lambda.1se',
parCvGlmnet = NULL, seed = NULL)
set the value of a tuning parameter of the bandit. param
is either
'lambdaRidge'
(the default) or 'lambdaLasso'
. Setting
lambdaLasso
> 0 enables a first stage of variable selection.
value
is either a scalar in [0,1] or 'auto'. If
set to 'auto'
, the parameter is picked using cv.glmnet
.
lambdaAuto
is either 'lambda.1se'
or 'lambda.min'
depending on which outcome of cv.glmnet
should be selected. It
is ignored if value
is not 'auto'
. parCvGlmnet
is a list of
parameters passed to cv.glmnet
. This parameter is ignored if
value
is not 'auto'
.
addSamples(df)
add samples to the bandit. df
is coercible to
a data.frame, and can be appended to the data.frame
used at creation.
In particular, it contains an id
column that is a primary key.
addOutcomes(y)
add outcomes to the bandit. y
is a named vector
whose names are samples ids.
undo()
cancel the last job.
bandit
, bandit_thompson
vignette("introduction", "banditr")
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