ftrlprox.formula: FTRL Proximal formula

In while/FTRLProximal: FTRL Proximal Implementation for Elastic Net Regression

Description

Online elastic net regression using the FTRL Proximal algorithm for training.

Usage

## S3 method for class 'formula'
ftrlprox(formula, data, lambda, alpha, a, b = 1,
  num_epochs = 1, save_loss = F, ...)

Arguments

formula	modeling formula
data	data.frame containing features and dependent variable
lambda	regularization term
alpha	mixing parameter, alpha=0 corresponds to L2 regularization and alpha=1 to L1.
a	learning rate parameter
b	learning rate parameter controlling decay, defaults to 1.
num_epochs	number of times we should traverse over the traiing set, defaults to 1.
save_loss	is to save the loss function during training.
...	additional args

Details

Test text

Value

ftrlprox model object

Author(s)

Vilhelm von Ehrenheim

Examples

require(mlbench)

p <- mlbench.circle(100,2)
dat <- as.data.frame(p)

mdl <- ftrlprox(classes ~ x.1 + x.2 + I(x.1^2) + I(x.2^2), dat,
                a = 0.3, lambda = 5.0, alpha = 1.0)
print(mdl)

while/FTRLProximal documentation built on May 4, 2019, 5:23 a.m.