dg_batch_seq: Gradient descent algorithm

In Beuleup93/dgrGlm: Regression logistic binary tools

Description

Gradient descent algorithm

Usage

dg_batch_seq(X, y, theta, leaning_rate, max_iter, tolerance, rho = NA, C = NA)

Arguments

X	is the matrix of our predictor variables with the bias column
y	is a target variable to predict.
theta	is a vector containing the parameters or coefficient of the logistic to be estimated
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
rho	hyper parameter which allows arbitration between RDIGE and LASSO.
C	parameter allowing to arbitrate between the penalty and the likelihood in the guidance of the modeling.

Value

this function returns the instance of model with all parameters

Author(s)

"Saliou NDAO [e-mail]"

Examples

## Not run: 
  gradient(X,y,theta)
  gradient(X,y,theta, leaning_rate=0.1, max_iter=100, tolerance=1e-04)

## End(Not run)

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