emil.fit.randomForest

Dataset, numerical matrix with observations as rows.

Whether to calculate permuted OOB error as a variable
importance measure, see <code><a href="/link/importance.randomForest?package=emil&version=1.1-6&to=randomForest" rd-options="randomForest" data-mini-rdoc="randomForest::importance.randomForest">importance.randomForest</a></code>. Set to
<code>FALSE</code> to speed up computation.

importance

Sent to <code><a href="/link/randomForest?package=emil&version=1.1-6&to=randomForest" rd-options="randomForest" data-mini-rdoc="randomForest::randomForest">randomForest</a></code>.

Directly calling the <code>randomForest</code> package implementation. See
<code><a href="/link/randomForest?package=emil&version=1.1-6&to=randomForest" rd-options="randomForest" data-mini-rdoc="randomForest::randomForest">randomForest</a></code> for parameter specification.

A toolbox for designing and evaluating predictive models with
    resampling methods. The aim of this package is to provide a simple and
    efficient general framework for working with any type of prediction
    problem, be it classification, regression or survival analysis, that is
    easy to extend and adapt to your specific setting. Some commonly used
    methods for classification, regression and survival analysis are included.

emil.fit.randomForest: Fit random forest.

Description

Usage

Arguments

Value

See Also