X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=man%2FAgghooCV.Rd;h=97d4c41eb273adb5e851b78d382e46613ee9da51;hb=a7ec4f8a3987ee66daef8471ed1a7a609a987914;hp=4d4cf78b7d4d84c614c6c447a1125335709489bc;hpb=cca5f1c67bd622fb7bc1279dfe4c3336d1446efd;p=agghoo.git diff --git a/man/AgghooCV.Rd b/man/AgghooCV.Rd index 4d4cf78..97d4c41 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 = NA)}\if{html}{\out{
}} +\if{html}{\out{
}}\preformatted{AgghooCV$new(data, target, task, gmodel, loss)}\if{html}{\out{
}} } \subsection{Arguments}{ @@ -32,12 +33,14 @@ Create a new AgghooCV object. \item{\code{target}}{Vector of targets (generally numeric or factor)} -\item{\code{task}}{"regression" or "classification"} +\item{\code{task}}{"regression" or "classification". +Default: classification if target not numeric.} -\item{\code{gmodel}}{Generic model returning a predictive function} +\item{\code{gmodel}}{Generic model returning a predictive function +Default: tree if mixed data, knn/ppr otherwise.} -\item{\code{quality}}{Function assessing the quality of a prediction; -quality(y1, y2) --> real number} +\item{\code{loss}}{Function assessing the error of a prediction +Default: error rate or mean(abs(error)).} } \if{html}{\out{}} } @@ -48,24 +51,20 @@ quality(y1, y2) --> real number} \subsection{Method \code{fit()}}{ Fit an agghoo model. \subsection{Usage}{ -\if{html}{\out{
}}\preformatted{AgghooCV$fit( - CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE), - mode = "agghoo" -)}\if{html}{\out{
}} +\if{html}{\out{
}}\preformatted{AgghooCV$fit(CV = NULL)}\if{html}{\out{
}} } \subsection{Arguments}{ \if{html}{\out{
}} \describe{ -\item{\code{CV}}{List describing cross-validation to run. Slots: -- type: 'vfold' or 'MC' for Monte-Carlo (default: MC) -- V: number of runs (default: 10) -- test_size: percentage of data in the test dataset, for MC - (irrelevant for V-fold). Default: 0.2. -- shuffle: wether or not to shuffle data before V-fold. - Irrelevant for Monte-Carlo; default: TRUE} - -\item{\code{mode}}{"agghoo" or "standard" (for usual cross-validation)} +\item{\code{CV}}{List describing cross-validation to run. Slots: \cr + - type: 'vfold' or 'MC' for Monte-Carlo (default: MC) \cr + - V: number of runs (default: 10) \cr + - test_size: percentage of data in the test dataset, for MC + (irrelevant for V-fold). Default: 0.2. \cr + - shuffle: wether or not to shuffle data before V-fold. + Irrelevant for Monte-Carlo; default: TRUE \cr +Default (if NULL): type="MC", V=10, test_size=0.2} } \if{html}{\out{
}} } @@ -76,19 +75,26 @@ Fit an agghoo model. \subsection{Method \code{predict()}}{ Predict an agghoo model (after calling fit()) \subsection{Usage}{ -\if{html}{\out{
}}\preformatted{AgghooCV$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{}}