X-Git-Url: https://git.auder.net/?p=agghoo.git;a=blobdiff_plain;f=man%2FAgghooCV.Rd;h=75ce9dbe11aa1caa345954620166b043d0b13804;hp=75d1ab6a3d83e968c3e870ae1729c2fe1ef6442e;hb=43a6578d444f388d72755e74c7eed74f3af638ec;hpb=d9a139b51ee2e71e13d67cb9d530834b15058617
diff --git a/man/AgghooCV.Rd b/man/AgghooCV.Rd
index 75d1ab6..75ce9db 100644
--- a/man/AgghooCV.Rd
+++ b/man/AgghooCV.Rd
@@ -13,6 +13,7 @@ from the list of models (see 'Model' class).
\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$clone()}}
}
}
@@ -22,7 +23,7 @@ from the list of models (see 'Model' class).
\subsection{Method \code{new()}}{
Create a new AgghooCV object.
\subsection{Usage}{
-\if{html}{\out{
}}\preformatted{AgghooCV$new(data, target, task, gmodel, quality = NULL)}\if{html}{\out{
}}
+\if{html}{\out{}}\preformatted{AgghooCV$new(data, target, task, gmodel, loss = NULL)}\if{html}{\out{
}}
}
\subsection{Arguments}{
@@ -36,8 +37,7 @@ Create a new AgghooCV object.
\item{\code{gmodel}}{Generic model returning a predictive function}
-\item{\code{quality}}{Function assessing the quality of a prediction;
-quality(y1, y2) --> real number}
+\item{\code{loss}}{Function assessing the error of a prediction}
}
\if{html}{\out{}}
}
@@ -48,24 +48,19 @@ quality(y1, y2) --> real number}
\subsection{Method \code{fit()}}{
Fit an agghoo model.
\subsection{Usage}{
-\if{html}{\out{}}\preformatted{AgghooCV$fit(
- CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE),
- mode = "agghoo"
-)}\if{html}{\out{
}}
+\if{html}{\out{}}\preformatted{AgghooCV$fit(CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE))}\if{html}{\out{
}}
}
\subsection{Arguments}{
\if{html}{\out{}}
\describe{
-\item{\code{CV}}{List describing cross-validation to run. Slots:
-- type: 'vfold' or 'MC' for Monte-Carlo (default: MC)
-- V: number of runs (default: 10)
-- test_size: percentage of data in the test dataset, for MC
+\item{\code{CV}}{List describing cross-validation to run. Slots: \cr
+- type: 'vfold' or 'MC' for Monte-Carlo (default: MC) \cr
+- V: number of runs (default: 10) \cr
+- test_size: percentage of data in the test dataset, for MC \cr
(irrelevant for V-fold). Default: 0.2.
- shuffle: wether or not to shuffle data before V-fold.
Irrelevant for Monte-Carlo; default: TRUE}
-
-\item{\code{mode}}{"agghoo" or "standard" (for usual cross-validation)}
}
\if{html}{\out{
}}
}
@@ -76,19 +71,26 @@ Fit an agghoo model.
\subsection{Method \code{predict()}}{
Predict an agghoo model (after calling fit())
\subsection{Usage}{
-\if{html}{\out{}}\preformatted{AgghooCV$predict(X, weight = "uniform")}\if{html}{\out{
}}
+\if{html}{\out{}}\preformatted{AgghooCV$predict(X)}\if{html}{\out{
}}
}
\subsection{Arguments}{
\if{html}{\out{}}
\describe{
\item{\code{X}}{Matrix or data.frame to predict}
-
-\item{\code{weight}}{"uniform" (default) or "quality" to weight votes or
-average models performances (TODO: bad idea?!)}
}
\if{html}{\out{
}}
}
+}
+\if{html}{\out{
}}
+\if{html}{\out{}}
+\if{latex}{\out{\hypertarget{method-getParams}{}}}
+\subsection{Method \code{getParams()}}{
+Return the list of V best parameters (after calling fit())
+\subsection{Usage}{
+\if{html}{\out{}}\preformatted{AgghooCV$getParams()}\if{html}{\out{
}}
+}
+
}
\if{html}{\out{
}}
\if{html}{\out{}}