| grid_search_1d {structToolbox} | R Documentation |
Carries out a grid search for a single parameter to try and identify the 'best' value for the parameter based on the input metric.
grid_search_1d( param_to_optimise, search_values, model_index, factor_name, max_min = "min", ... )
param_to_optimise |
The name of an input parameter of the model the optimise |
search_values |
A vector of values to search for the optimum |
model_index |
A number indicating which step of a model_seq is to be optimised |
factor_name |
The sample_meta column name to use |
max_min |
'A string 'max' or 'min' to indicate whether to maximise or minimise the metric |
... |
additional slots and values passed to struct_class |
struct object
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())