make_folds

Generate folds for CV out of one hot encoded matrix

Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as
proposed by Ruegamer et al. (2023) <doi:10.18637/jss.v105.i02>.
Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.

David Ruegamer

deepregression

Fitting Deep Distributional Regression

Christopher Marquardt

Laetitia Frost

Florian Pfisterer

Philipp Baumann

Chris Kolb

Lucas Kook

make_folds function

<dl><dt>mat</dt>
<dd>matrix with columns corresponding to folds
and entries corresponding to a one hot encoding</dd>
<dt>val_train</dt>
<dd>the value corresponding to train, per default 0</dd>
<dt>val_test</dt>
<dd>the value corresponding to test, per default 1</dd></dl>

Arguments

Generate folds for CV out of one hot encoded matrix — make_folds

<dl>

<dt>mat</dt>
<dd>matrix with columns corresponding to folds
and entries corresponding to a one hot encoding</dd>


<dt>val_train</dt>
<dd>the value corresponding to train, per default 0</dd>


<dt>val_test</dt>
<dd>the value corresponding to test, per default 1</dd>

</dl>

make_folds: Generate folds for CV out of one hot encoded matrix

Description

Usage

Arguments

Details