ModelSpecification-methods: Model Specification

In MachineShop: Machine Learning Models and Tools

Model Specification

Description

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

Usage

ModelSpecification(...)

## Default S3 method:
ModelSpecification(
  input,
  model,
  control = MachineShop::settings("control"),
  metrics = NULL,
  cutoff = MachineShop::settings("cutoff"),
  stat = MachineShop::settings("stat.TrainingParams"),
  ...
)

## S3 method for class 'formula'
ModelSpecification(formula, data, model, ...)

## S3 method for class 'matrix'
ModelSpecification(x, y, model, ...)

## S3 method for class 'ModelFrame'
ModelSpecification(input, model, ...)

## S3 method for class 'recipe'
ModelSpecification(input, model, ...)

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.

Value

ModelSpecification class object.

See Also

fit, resample, set_monitor, set_optim

Examples

## Requires prior installation of suggested package gbm to run

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

MachineShop documentation built on Sept. 18, 2023, 5:06 p.m.