train_preds

Obtain predictions on outer training folds which can be used for performance
metrics and ROC curves.

Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package <doi:10.1093/bioadv/vbad048>. Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) <doi:10.1111/j.2517-6161.1977.tb01603.x>. Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) <doi:10.1214/17-EJS1337SI>.

Myles Lewis

nestedcv

Nested Cross-Validation with 'glmnet' and 'caret'

Athina Spiliopoulou

Cankut Cubuk

David Rios Santini

Katriona Goldmann

Ryan C. Thompson

train_preds function

<dl><dt>x</dt>
<dd>a <code>nestcv.glmnet</code>, <code>nestcv.train</code> or <code>outercv</code> fitted object</dd></dl>

Arguments

Outer training fold predictions — train_preds

<dl>

<dt>x</dt>
<dd>a <code>nestcv.glmnet</code>, <code>nestcv.train</code> or <code>outercv</code> fitted object</dd>

</dl>

train_preds: Outer training fold predictions

Description

Usage

Value

Arguments

Details