MachineShop (version 3.8.0)

ModelSpecification: Model Specification

Description

Specification of a relationship between response and predictor variables and a model to define a relationship between them.

Usage

ModelSpecification(...)
# S3 method for default
ModelSpecification(
  input,
  model,
  control = MachineShop::settings("control"),
  metrics = NULL,
  cutoff = MachineShop::settings("cutoff"),
  stat = MachineShop::settings("stat.TrainingParams"),
  ...
)
# S3 method for formula
ModelSpecification(formula, data, model, ...)
# S3 method for matrix
ModelSpecification(x, y, model, ...)
# S3 method for ModelFrame
ModelSpecification(input, model, ...)
# S3 method for recipe
ModelSpecification(input, model, ...)

Value

ModelSpecification class object.

Arguments

...

arguments passed from the generic function to its methods. The first argument of each ModelSpecification method is positional and, as such, must be given first in calls to them.

input

input object defining and containing the model predictor and response variables.

model

model function, function name, or object; or another object that can be coerced to a model.

control

control function, function name, or object defining the resampling method to be employed. If NULL or if the model specification contains any SelectedInput or SelectedModel objects, then object-specific control structures and training parameters are used for selection and tuning, as usual, and objects are trained sequentially with nested resampling. Otherwise,

• tuning of input and model objects is performed simultaneously over a global grid of their parameter values, and

• the specified control method and training parameters below override those of any included TunedInput or TunedModel.

metrics

metric function, function name, or vector of these with which to calculate performance. If not specified, default metrics defined in the performance functions are used. Model selection is based on the first calculated metric.

cutoff

argument passed to the metrics functions.

stat

function or character string naming a function to compute a summary statistic on resampled metric values for model tuning.

formula, data

formula defining the model predictor and response variables and a data frame containing them.

x, y

matrix and object containing predictor and response variables.

See Also

fit, resample, set_monitor, set_optim

Examples

# \donttest{
## Requires prior installation of suggested package gbm to run

modelspec <- ModelSpecification(
  sale_amount ~ ., data = ICHomes, model = GBMModel
)
fit(modelspec)
# }