TunedModel {MachineShop}

Tuned Model

Description

Model tuning over a grid of parameter values.

Usage

TunedModel(
  object,
  grid = MachineShop::settings("grid"),
  control = MachineShop::settings("control"),
  metrics = NULL,
  cutoff = MachineShop::settings("cutoff"),
  stat = MachineShop::settings("stat.TrainingParams")
)

Arguments

object

model function, function name, or object defining the model to be tuned.

grid

single integer or vector of integers whose positions or names match the parameters in the model's pre-defined tuning grid if one exists and which specify the number of values used to construct the grid; TuningGrid function, function name, or object; ParameterGrid object; or data frame containing parameter values at which to evaluate the model, such as that returned by expand_params.

control

control function, function name, or object defining the resampling method to be employed.

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.

Details

The expand_modelgrid function enables manual extraction and viewing of grids created automatically when a TunedModel is fit.

Response types:

factor, numeric, ordered, Surv

Value

TunedModel class object that inherits from MLModel.

See Also

fit, resample, set_optim

Examples

## Requires prior installation of suggested package gbm to run
## May require a long runtime

# Automatically generated grid
model_fit <- fit(sale_amount ~ ., data = ICHomes,
                 model = TunedModel(GBMModel))
varimp(model_fit)
(tuned_model <- as.MLModel(model_fit))
summary(tuned_model)
plot(tuned_model, type = "l")

# Randomly sampled grid points
fit(sale_amount ~ ., data = ICHomes,
    model = TunedModel(
      GBMModel,
      grid = TuningGrid(size = 1000, random = 5)
    ))

# User-specified grid
fit(sale_amount ~ ., data = ICHomes,
    model = TunedModel(
      GBMModel,
      grid = expand_params(
        n.trees = c(50, 100),
        interaction.depth = 1:2,
        n.minobsinnode = c(5, 10)
      )
    ))