prediction.output-class

The object of this class stores predictions and predictive accuracy
estimates.

classes

Functions to implements random forest method for model based
recursive partitioning. The mob() function, developed by Zeileis et al. (2008),
within 'party' package, is modified to construct model-based decision trees based
on random forests methodology. The main input function mobforest.analysis() takes
all input parameters to construct trees, compute out-of-bag errors, predictions,
and overall accuracy of forest. The algorithm performs parallel computation
using cluster functions within 'parallel' package.

Kasey Jones

mobForest

Model Based Random Forest Analysis

prediction.output-class function

Objects can be created by calls of the
form <code><a rd-options="" href="/link/prediction.output?package=mobForest&version=1.3.1" data-mini-rdoc="mobForest::prediction.output">prediction.output</a></code>.

Objects from the Class

Class <code>"prediction.output"</code> of mobForest model — prediction.output-class

Objects can be created by calls of the
form <code><a rd-options='' href='prediction.output'>prediction.output</a></code>.

prediction.output-class: Class `"prediction.output"` of mobForest model

Description

Arguments

Objects from the Class

See Also