grid_search_1d {structToolbox} | R Documentation

## One dimensional grid search

### Description

A one dimensional grid search calculates a performance metric for a model at evenly spaced values for a model input parameter. The "optimum" value for the parameter is suggested as the one which maximises performance, or minimises error (whichever is appropriate for the chosen metric)

### Usage

    grid_search_1d(
      param_to_optimise,
      search_values,
      model_index,
      factor_name,
      max_min = "min",
      ...
    )

### Arguments

`param_to_optimise` | (character) The name of the model input parameter that is the focus of the search.
`search_values` | (ANY) The values of the input parameter being optimised.
`model_index` | (numeric, integer) The index of the model in the sequence that uses the parameter being optimised.
`factor_name` | (character) The name of a sample-meta column to use.
`max_min` | (character) Maximise or minimise. Allowed values are limited to the following: `"max"`: The optimium parameter value is suggested based on maximising the performance metric. `"min"`: The optimium parameter value is suggested based on minimising the performance metric. The default is `"min"`.
`...` | Additional slots and values passed to `struct_class`.

### Value

A `grid_search_1d` object.

### Examples

    D = MTBLS79_DatasetExperiment()
    # some preprocessing
    M = pqn_norm(qc_label='QC',factor_name='class') +
        knn_impute() +
        glog_transform(qc_label='QC',factor_name='class') +
        filter_smeta(factor_name='class',levels='QC',mode='exclude')
    M=model_apply(M,D)
    D=predicted(M)

    # reduce number of features for this example
    D=D[,1:10]

    # optmise number of components for PLS model
    I = grid_search_1d(param_to_optimise='number_components',search_values=1:5,
            model_index=2,factor_name='class') *
            (mean_centre()+PLSDA(factor_name='class'))
    I = run(I,D,balanced_accuracy())

[Package structToolbox version 1.2.0 Index]