weight

Calculate weights for class imbalance

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

weight function

<dl><dt>y</dt>
<dd>Factor or character response vector. If a character vector is
supplied it is coerced into a factor. Unused levels are dropped.</dd></dl>

Arguments

Calculate weights for class imbalance — weight

<dl>

<dt>y</dt>
<dd>Factor or character response vector. If a character vector is
supplied it is coerced into a factor. Unused levels are dropped.</dd>

</dl>

weight: Calculate weights for class imbalance

Description

Usage

Value

Arguments