partial_fit_logistic

A `stoch_logistic` object as output by function `stochastic.logistic.regression`.
Will be modified in-place.

logistic_model

Data with covariates. If passing a `data.frame`, the model object must have been initialized
with a formula, and `X` must also contain the target variable (`y`). If passing a matrix, must
also pass `y`. Note that whatever factor levels are present in the first batch of data, will be taken as the
whole factor levels.

The target variable, when using matrices. Ignored when using formula.

Sample weights (optional). If required, must pass them at every partial fit iteration.

Perform a quasi-Newton iteration to update the model with new data.

Implementations of stochastic, limited-memory quasi-Newton optimizers,
similar in spirit to the LBFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm,
for smooth stochastic optimization. Implements the following methods:
oLBFGS (online LBFGS) (Schraudolph, N.N., Yu, J. and Guenter, S., 2007 <http://proceedings.mlr.press/v2/schraudolph07a.html>),
SQN (stochastic quasi-Newton) (Byrd, R.H., Hansen, S.L., Nocedal, J. and Singer, Y., 2016 <arXiv:1401.7020>),
adaQN (adaptive quasi-Newton) (Keskar, N.S., Berahas, A.S., 2016, <arXiv:1511.01169>).
Provides functions for easily creating R objects
with partial_fit/predict methods from some given objective/gradient/predict functions.
Includes an example stochastic logistic regression using these optimizers.
Provides header files and registered C routines for using it directly from C/C++.

partial_fit_logistic: Update stochastic logistic regression model with new batch of data

Description

Usage

Arguments

Value

See Also