dgrglm.multiclass.fit: Logistic regression multiclass

In Beuleup93/dgrGlm: Regression logistic binary tools

Description

This function allows us to create a binary logistic regression model

Usage

dgrglm.multiclass.fit(
  formule,
  data,
  leaning_rate = 0.1,
  max_iter = 3000,
  tolerance = 1e-04,
  random_state = 102,
  centering = 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
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
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

Value

an instance of model

Examples

## Not run: 
 dgrglm.multiclass.fit(formule, data)

## End(Not run)

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