terda
terda.Rdterbeam is a model search algorithm.
Usage
terda(
sparsity = 5,
nIterations = 5,
max.nb.features = 1000,
kBest = "NULL",
method = "glmnetRR",
kStep = "NULL",
vartype = "real",
gamma = 0.7,
nRR = 1,
lb = -1,
ub = 1,
language = "terinter",
scoreFormula = scoreRatio,
epsilon = "NULL",
nblambdas = 1000,
objective = "auc",
evalToFit = "auc_",
k_penalty = 0,
intercept = "NULL",
popSaveFile = "NULL",
final.pop.perc = 100,
alpha = 0.5,
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.
- nIterations:
??
- max.nb.features:
create the glmnet object using only the top most significant features (default:1000)
- kBest:
??
- method:
??
- kStep:
??
- vartype:
(??)
- gamma:
??
- nRR:
(??) (default:FALSE)
- lb:
??
- ub:
??
- 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)
- 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)
- popSaveFile:
(??)
- final.pop.perc:
??
- 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 out debug infotmation when activated (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"