standardCV_run

Run and eval the standard cross-validation procedure.

The 'agghoo' procedure is an alternative to usual cross-validation.
Instead of choosing the best model trained on V subsamples, it determines
a winner model for each subsample, and then aggregates the V outputs.
For the details, see "Aggregated hold-out" by Guillaume Maillard,
Sylvain Arlot, Matthieu Lerasle (2021) <arXiv:1909.04890>
published in Journal of Machine Learning Research 22(20):1--55.

Benjamin Auder

agghoo

Aggregated Hold-Out Cross Validation

Sylvain Arlot

Melina Gallopin

Matthieu Lerasle

Guillaume Maillard

standardCV_run function

<dl><dt>dataTrain</dt>
<dd>Train dataset</dd>
<dt>dataTest</dt>
<dd>Test dataset</dd>
<dt>targetTrain</dt>
<dd>Train targets</dd>
<dt>targetTest</dt>
<dd>Test targets</dd>
<dt>floss</dt>
<dd>Loss function to compute error on test dataset</dd>
<dt>verbose</dt>
<dd>Show some execution trace</dd>
<dt>...</dt>
<dd>List defining the model (gmodel) and its parameters (params)</dd></dl>

Arguments

standardCV_run — standardCV_run

<dl>

<dt>dataTrain</dt>
<dd>Train dataset</dd>


<dt>dataTest</dt>
<dd>Test dataset</dd>


<dt>targetTrain</dt>
<dd>Train targets</dd>


<dt>targetTest</dt>
<dd>Test targets</dd>


<dt>floss</dt>
<dd>Loss function to compute error on test dataset</dd>


<dt>verbose</dt>
<dd>Show some execution trace</dd>


<dt>...</dt>
<dd>List defining the model (gmodel) and its parameters (params)</dd>

</dl>

standardCV_run: standardCV_run

Description

Usage

Arguments