metal | R Documentation |
metal is a model search algorithm on a list of beam search approach and get the populations into GA.
metal(
sparsity = 1:10,
max.nb.features = 1000,
popSaveFile = "NULL",
saveFiles = FALSE,
pathSave = "NULL",
language = "mix",
scoreFormula = scoreRatio,
epsilon = "NULL",
objective = "auc",
k_penalty = 0,
evalToFit = "accuracy_",
estimate_coefs = FALSE,
intercept = "NULL",
testAllSigns = FALSE,
plot = FALSE,
verbose = TRUE,
warnings = FALSE,
debug = FALSE,
print_ind_method = "short",
parallelize.folds = TRUE,
nCores = 10,
seed = "NULL",
experiment.id = "NULL",
experiment.description = "NULL",
experiment.save = "nothing",
list.clfs = "NULL",
unificator.method = "terga2",
unificator.evolver = "v2m_new"
)
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. |
max.nb.features: |
focuses only on the subset of top most significant features (default:1000) |
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) |
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 graphics indicating the evolution of the simulation (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"). |
parallelize.folds: |
parallelize folds when cross-validating (default:TRUE) |
nCores: |
the number of cores to execute the program. If nCores=1 than the program runs in a non parallel mode |
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" |
list.clfs: |
list of Genetor and Unificator |
unificator.method: |
the default unificator is a terga2. Other one specified will yield a stop of the program. |
unificator.evolver: |
the default evolve method used by the unificator which is by default a terga2. |
an object containing a list of parameters for this classifier
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