ga_snpls: Genetic Algorithm for selection of hyperparameter values

In David-Hervas/sNPLS: NPLS Regression with L1 Penalization

Genetic Algorithm for selection of hyperparameter values

Description

Runs a genetic algorithm to select the best combination of hyperparameter values

Usage

ga_snpls(
  X,
  Y,
  ncomp = c(1, 3),
  threshold_j = c(0, 1),
  threshold_k = c(0, 1),
  maxiter = 20,
  popSize = 50,
  parallel = TRUE,
  replicates = 10,
  metric = "RMSE",
  method = "sNPLS",
  ...
)

Arguments

X	A three-way array containing the predictors.
Y	A matrix containing the response.
ncomp	A vector with the minimum and maximum number of components to assess
threshold_j	Vector with threshold min and max values on Wj. Scaled between [0, 1)
threshold_k	Vector with threshold min and max values on Wk. Scaled between [0, 1)
maxiter	Maximum number of iterations (generations) of the genetic algorithm
popSize	Population size (see GA::ga() documentation)
parallel	Should the computations be performed in parallel? (see GA::ga() documentation)
replicates	Number of replicates for the cross-validation performed in the fitness function of the genetic algoritm
metric	Select between RMSE or AUC (for 0/1 response)
method	Select between sNPLS, sNPLS-SR or sNPLS-VIP
...	Further arguments passed to GA::ga()

Value

A summary of the genetic algorithm results

David-Hervas/sNPLS documentation built on Feb. 1, 2024, 6:30 a.m.