terbeam

Usage

terBeam(
  sparsity = 1:5,
  max.nb.features = 1000,
  maxNbOfModels = 10000,
  nbBest = round(maxNbOfModels/10),
  nbVeryBest = round(maxNbOfModels/100),
  final.pop.perc = 100,
  popSaveFile = "NULL",
  saveFiles = FALSE,
  language = "terinter",
  scoreFormula = scoreRatio,
  epsilon = "NULL",
  objective = "auc",
  k_penalty = 0,
  evalToFit = "auc_",
  estimate_coefs = FALSE,
  intercept = "NULL",
  testAllSigns = FALSE,
  plot = FALSE,
  verbose = TRUE,
  warnings = FALSE,
  debug = FALSE,
  print_ind_method = "short",
  parallelize.folds = TRUE,
  nCores = 4,
  seed = "NULL",
  experiment.id = "NULL",
  experiment.description = "NULL",
  experiment.save = "nothing"
)

Arguments

language

is the language that is used by the different algorithms bin, bininter, ter, terinter, ratio, (default:"terinter")

sparsity:

number of features in a given model. This is a vector with multiple lengths.

maxNbOfModels:

number of models to be explored for a given k_sparsity. This is equivalent to a population size in terga.

nbVeryBest:

is the number of features to be kept that appear in the very best models. They will be kept even if they are not frequent in the best models (default: 1 percent of maxNbOfModels).

nbBest:

is the number of features that will be used to build the k+1 sparsity combinations (default: 10 percent of maxNbOfModels).

final.pop.perc:

a percentage of nbVeryBest translates in a number of models to be kept for k_sparsity.

popSaveFile:

(??)

saveFiles:

??

scoreFormula:

a Function that contains the ratio Formula or other specific ones

epsilon:

a small value to be used with the ratio language (useCustomLanguage) (default: NULL). When null it is going to be calculated by the minimum value of X divided by 10.

objective:

this can be auc, cor or aic. Terga can also predict regression, other than class prediction. (default:auc)

max.nb.features:

focuses only on the subset of top most significant features (default:1000)

estimate_coefs:

non ternary solution for the aic objective (default:FALSE)

evalToFit:

The model performance attribute to use as fitting score (default:"fit_"). Other choices are c("auc_","accuracy_","precision_","recall_","f_score_")

k_penalty:

Penalization of the fit by the k_sparsity (default: 0)

intercept:

(??) (default:NULL)

testAllSigns:

??

plot:

Plot different graphics (default:FALSE).

verbose:

print out information on the progress of the algorithm (default:TRUE)

warnings:

Print out warnings when runnig (default:FALSE).

debug:

print debug information (default:FALSE)

print_ind_method:

One of c("short","graphical") indicates how to print a model and subsequently a population during the run (default:"short").

nCores:

the number of cores to execute the program. If nCores=1 than the program runs in a non parallel mode

parallelize.folds:

parallelize folds when cross-validating (default:TRUE)

seed:

the seed to be used for reproductibility. If seed=NULL than it is not taken into account (default:NULL).

experiment.id:

The id of the experiment that is to be used in the plots and comparitive analyses (default is the learner's name, when not specified)

experiment.description:

A longer description of the experiment. This is important when many experiments are run and can also be printed in by the printExperiment function.

experiment.save:

Data from an experiment can be saved with different levels of completness, with options to be selected from c("nothing", "minimal", "full"), default is "minimal"

parallel:

parallel

Value

an object containing a list of parameters for this classifier

Details

terbeam: ternary beam searching algorithm