dot-fit.nnet: Neural Network regression for 'tidyfit'

In tidyfit: Regularized Linear Modeling with Tidy Data

Neural Network regression for tidyfit

Description

Fits a single-hidden-layer neural network regression on a 'tidyFit' R6 class. The function can be used with regress and classify.

Usage

## S3 method for class 'nnet'
.fit(self, data = NULL)

Arguments

self	a 'tidyFit' R6 class.
data	a data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).

Details

Hyperparameters:

• size (number of units in the hidden layer)

• decay (parameter for weight decay)

• maxit (maximum number of iterations)

Important method arguments (passed to m)

The function provides a wrapper for nnet::nnet.formula. See ?nnet for more details.

Implementation

For regress, linear output units (linout=True) are used, while classify implements the default logic of nnet (entropy=TRUE for 2 target classes and softmax=TRUE for more classes).

Value

A fitted 'tidyFit' class model.

Author(s)

Phil Holzmeister

Examples

# Load data
data <- tidyfit::Factor_Industry_Returns

# Stand-alone function
fit <- m("nnet", Return ~ ., data)
fit

# Within 'regress' function
fit <- regress(data, Return ~ ., m("nnet", decay=0.5, size = 8),
               .mask = c("Date", "Industry"))

# Within 'classify' function
fit <- classify(iris, Species ~ ., m("nnet", decay=0.5, size = 8))

tidyfit documentation built on April 4, 2025, 4:38 a.m.