details_decision_tree_C5.0.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/decision_tree_C5.0.R
\name{details_decision_tree_C5.0}
\alias{details_decision_tree_C5.0}
\title{Decision trees via C5.0}
\description{
\code{\link[C50:C5.0]{C50::C5.0()}} fits a model as a set of \verb{if/then} statements that
creates a tree-based structure.
}
\details{
For this engine, there is a single mode: classification
\subsection{Tuning Parameters}{

This model has 1 tuning parameters:
\itemize{
\item \code{min_n}: Minimal Node Size (type: integer, default: 2L)
}
}

\subsection{Translation from parsnip to the original package (classification)}{

\if{html}{\out{<div class="sourceCode r">}}\preformatted{decision_tree(min_n = integer()) \%>\% 
  set_engine("C5.0") \%>\% 
  set_mode("classification") \%>\% 
  translate()
}\if{html}{\out{</div>}}

\if{html}{\out{<div class="sourceCode">}}\preformatted{## Decision Tree Model Specification (classification)
## 
## Main Arguments:
##   min_n = integer()
## 
## Computational engine: C5.0 
## 
## Model fit template:
## parsnip::C5.0_train(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
##     minCases = integer(), trials = 1)
}\if{html}{\out{</div>}}

\code{\link[=C5.0_train]{C5.0_train()}} is a wrapper around
\code{\link[C50:C5.0]{C50::C5.0()}} that makes it easier to run this model.
}

\subsection{Preprocessing requirements}{

This engine does not require any special encoding of the predictors.
Categorical predictors can be partitioned into groups of factor levels
(e.g. \verb{\{a, c\}} vs \verb{\{b, d\}}) when splitting at a node. Dummy variables
are not required for this model.
}

\subsection{Case weights}{

This model can utilize case weights during model fitting. To use them,
see the documentation in \link{case_weights} and the examples
on \code{tidymodels.org}.

The \code{fit()} and \code{fit_xy()} arguments have arguments called
\code{case_weights} that expect vectors of case weights.
}

\subsection{Saving fitted model objects}{

This model object contains data that are not required to make
predictions. When saving the model for the purpose of prediction, the
size of the saved object might be substantially reduced by using
functions from the \href{https://butcher.tidymodels.org}{butcher} package.
}

\subsection{Examples}{

The “Fitting and Predicting with parsnip” article contains
\href{https://parsnip.tidymodels.org/articles/articles/Examples.html#decision-tree-C5.0}{examples}
for \code{decision_tree()} with the \code{"C5.0"} engine.
}

\subsection{References}{
\itemize{
\item Kuhn, M, and K Johnson. 2013. \emph{Applied Predictive Modeling}. Springer.
}
}
}
\keyword{internal}