predict.epenreg

Obtain model predictions from training or new data for <code>epenreg</code> model.

Extending the base classes and methods of EnsembleBase package for Penalized-Regression-based (Ridge and Lasso) integration of base learners. Default implementation uses cross-validation error to choose the optimal lambda (shrinkage parameter) for the final predictor. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in EnsembleBase package as well as this package.

Alireza Mahani

EnsemblePenReg

Extensible Classes and Methods for Penalized-Regression-Based
Integration of Base Learners

predict.epenreg function

<dl> <dt>object</dt>
<dd>Object of class <code>"epenreg"</code>, typically the output of function <code>epenreg</code>.</dd> <dt>newdata</dt>
<dd>New data frame to make predictions for. If <code>NULL</code>, prediction is made for training data.</dd> <dt>ncores</dt>
<dd>Number of cores to use for parallel prediction.</dd> <dt>...</dt>
<dd>Arguments passed to/from other methods.</dd></dl>

Arguments

Mansour T.A. Sharabiani, Alireza S. Mahani

Author

Predict method for class "epenreg" — predict.epenreg

<dl>

 <dt>object</dt>
<dd>Object of class <code>"epenreg"</code>, typically the output of function <code>epenreg</code>.</dd>

 <dt>newdata</dt>
<dd>New data frame to make predictions for. If <code>NULL</code>, prediction is made for training data.</dd>

 <dt>ncores</dt>
<dd>Number of cores to use for parallel prediction.</dd>

 <dt>...</dt>
<dd>Arguments passed to/from other methods.</dd>

</dl>

predict.epenreg: Predict method for class `"epenreg"`

Description

Usage

Value

Arguments

Author