Class encapsulating the methods to run to obtain the best predictor
from the list of models (see 'Model' class).
}
+\section{Public fields}{
+\if{html}{\out{<div class="r6-fields">}}
+\describe{
+\item{\code{params}}{List of parameters of the V selected models}
+}
+\if{html}{\out{</div>}}
+}
\section{Methods}{
\subsection{Public methods}{
\itemize{
\item \href{#method-new}{\code{AgghooCV$new()}}
\item \href{#method-fit}{\code{AgghooCV$fit()}}
\item \href{#method-predict}{\code{AgghooCV$predict()}}
+\item \href{#method-getParams}{\code{AgghooCV$getParams()}}
\item \href{#method-clone}{\code{AgghooCV$clone()}}
}
}
\subsection{Method \code{new()}}{
Create a new AgghooCV object.
\subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$new(data, target, task, gmodel, quality = NULL)}\if{html}{\out{</div>}}
+\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$new(data, target, task, gmodel, loss = NULL)}\if{html}{\out{</div>}}
}
\subsection{Arguments}{
\item{\code{gmodel}}{Generic model returning a predictive function}
-\item{\code{quality}}{Function assessing the quality of a prediction;
-quality(y1, y2) --> real number}
+\item{\code{loss}}{Function assessing the error of a prediction}
}
\if{html}{\out{</div>}}
}
\subsection{Method \code{fit()}}{
Fit an agghoo model.
\subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$fit(
- CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE),
- mode = "agghoo"
-)}\if{html}{\out{</div>}}
+\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$fit(CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE))}\if{html}{\out{</div>}}
}
\subsection{Arguments}{
(irrelevant for V-fold). Default: 0.2.
- shuffle: wether or not to shuffle data before V-fold.
Irrelevant for Monte-Carlo; default: TRUE}
-
-\item{\code{mode}}{"agghoo" or "standard" (for usual cross-validation)}
}
\if{html}{\out{</div>}}
}
\subsection{Method \code{predict()}}{
Predict an agghoo model (after calling fit())
\subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$predict(X, weight = "uniform")}\if{html}{\out{</div>}}
+\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$predict(X)}\if{html}{\out{</div>}}
}
\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{X}}{Matrix or data.frame to predict}
-
-\item{\code{weight}}{"uniform" (default) or "quality" to weight votes or
-average models performances (TODO: bad idea?!)}
}
\if{html}{\out{</div>}}
}
+}
+\if{html}{\out{<hr>}}
+\if{html}{\out{<a id="method-getParams"></a>}}
+\if{latex}{\out{\hypertarget{method-getParams}{}}}
+\subsection{Method \code{getParams()}}{
+Return the list of V best parameters (after calling fit())
+\subsection{Usage}{
+\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$getParams()}\if{html}{\out{</div>}}
+}
+
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-clone"></a>}}