dgrglm.fit: Function fit to construct model

In Beuleup93/dgrGlm: Regression logistic binary tools

Description

This function allows us to create a binary logistic regression model

Usage

dgrglm.fit(
  formule,
  data,
  ncores = NA,
  mode_compute = "parallel",
  leaning_rate = 0.1,
  max_iter = 100,
  tolerance = 1e-04,
  batch_size = NA,
  random_state = 102,
  centering = FALSE,
  feature_selection = FALSE,
  p_value = 0.01,
  rho = 0.1,
  C = 0.1,
  iselasticnet = FALSE
)

Arguments

formule	allows you to define the target variable and predictor variables
data	the data source containing all the variables specified in the formula
ncores	parameters representing the number of cores to be used for parallel execution
mode_compute	algorithm mode of execution. thera are "parallel" and "sequentiel"
leaning_rate	is the learning rate that controls the magnitude of the vector update
max_iter	is the number of iterations
tolerance	an additional parameter which specifies the minimum movement allowed for each iteration
batch_size	a parameter that specifies the number of observations in each mini-batch. It can significantly affect performance
random_state	this parameter defines the seed of the random number generator, use when shuffling to mix observations
centering	to center and reduce the variables, by default FALSE
feature_selection	parameters indicating the choice to make the selection of variable or not
p_value	selection criteria
rho	hyper parameter which allows arbitration between RDIGE and LASSO. Elasticnet case.
C	parameter allowing to arbitrate between the penalty and the likelihood in the guidance of the modeling.Elasticnet case.
iselasticnet	for Elasticnet

Value

an instance of model

Examples

## Not run: 
 dgrglm.fit(formule, data)
 dgrglm.fit(formule, data,ncores=3, mode_compute="parallel")

## End(Not run)

Beuleup93/dgrGlm documentation built on Dec. 17, 2021, 10:50 a.m.