update.ftrlprox: Update FTRL Proximal model

In FTRLProximal: FTRL Proximal Implementation for Elastic Net Regression

Description

Continue training model on new data

Usage

## S3 method for class 'ftrlprox'
update(object, newX, newY, num_epochs = 1, save_loss = F,
  ...)

Arguments

object	the model object
newX	new feature vectors. This needs to be the same features as used in previous training rounds for this object.
newY	new observations
num_epochs	number of times we should traverse over the training data, defaults to 1.
save_loss	is to save the loss function during training. This will be appended to previous loss vector.
...	additional args

Details

As FTRL PRoximal is an online algorithm it is possible to continue training the model on new data. This can be good if for for example the size of the dataset is too large to keep in memory or new data is getting available after some time.

Value

ftrlprox model object

Author(s)

Vilhelm von Ehrenheim

FTRLProximal documentation built on May 29, 2017, 5:39 p.m.