% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/modelizer.R
\name{modelizer}
\alias{modelizer}
\title{Generate a classification model}
\usage{
modelizer(
  dfm,
  outer_k,
  inner_k,
  class_type,
  opt_measure,
  country,
  grid,
  seed,
  model,
  we_vectors,
  cores = 1
)
}
\arguments{
\item{dfm}{A quanteda dfm used to train and evaluate the model, should contain the vector with class labels in docvars}

\item{outer_k}{Number of outer cross-validation folds (for performance estimation)}

\item{inner_k}{Number of inner cross-validation folds (for hyperparameter optimization and feature selection)}

\item{class_type}{Type of classification to model ("junk", "aggregate", or "codes")}

\item{opt_measure}{Label of measure in confusion matrix to use as performance indicator}

\item{country}{Two-letter country abbreviation of the country the model is estimated for (used for filename)}

\item{grid}{Data frame providing all possible combinations of hyperparameters and feature selection parameters for a given model (grid search)}

\item{seed}{Integer to use as seed for random number generation, ensures replicability}

\item{model}{Classification algorithm to use (currently only "nb" for Naïve Bayes using textmodel_nb)}

\item{we_vectors}{Matrix with word embedding vectors}

\item{cores}{Number of threads used for parallel processing using future_lapply, defaults to 1}
}
\value{
A list containing all relevant output
}
\description{
Generate a nested cross validated classification model based on a dfm with class labels as docvars
Currently only supports Naïve Bayes using quanteda's textmodel_nb
Hyperparemeter optimization is enabled through the grid parameter
A grid should be generated from vectors with the labels as described for each model, using the crossing() command
For Naïve Bayes, the following parameters can be used:
- percentiles (cutoff point for tf-idf feature selection)
- measures (what measure to use for determining feature importance, see textstat_keyness for options)
}
\examples{
modelizer(dfm, outer_k, inner_k, class_type, opt_measure, country, grid, seed, model, cores = 1)
}