% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parsnip_mlp.R
\name{ggmlr_parsnip_fit_classif}
\alias{ggmlr_parsnip_fit_classif}
\title{parsnip ggml engine: classification fit}
\usage{
ggmlr_parsnip_fit_classif(
  x,
  y,
  hidden_layers = c(128L, 64L),
  epochs = 10L,
  dropout = 0.2,
  activation = "relu",
  learn_rate = NULL,
  batch_size = 32L,
  verbose = 0L,
  validation_split = 0,
  callbacks = list(),
  optimizer = "adam",
  backend = "auto",
  ...
)
}
\arguments{
\item{x}{Numeric matrix of predictors.}

\item{y}{Factor of class labels.}

\item{hidden_layers}{Integer vector of hidden layer widths.}

\item{epochs}{Number of training epochs.}

\item{dropout}{Dropout rate in `[0, 1)`.}

\item{activation}{Hidden activation, e.g. `"relu"`.}

\item{learn_rate}{Optional learning rate (applied via callback).}

\item{batch_size}{Minibatch size.}

\item{verbose}{Verbosity level (0/1/2).}

\item{validation_split}{Fraction in `[0, 1)` for validation.}

\item{callbacks}{List of ggmlR callbacks.}

\item{optimizer}{One of `"adam"`, `"sgd"`.}

\item{backend}{One of `"auto"`, `"cpu"`, `"vulkan"`.}

\item{...}{Unused.}
}
\value{
A fitted `ggmlr_parsnip_model` object.
}
\description{
Internal fit wrapper called by parsnip when `mlp()` is used with
`engine = "ggml"` in classification mode. Not intended for direct use.
}
\keyword{internal}
