setup_GLMNET

Setup hyperparameters for GLMNET training.

Machine learning and visualization package with an 'S7' backend featuring
comprehensive type checking and validation, observable runs, and auditable outputs.
The functional API provides one-call access to supervised and unsupervised learning
through train(), cluster(), and decomp(). All configuration steps are performed using
setup functions and validated. A single call to train() can handle preprocessing,
decomposition, hyperparameter tuning, model stacking, and testing with nested resampling.
Supports 'data.frame', 'data.table', and 'tibble' inputs and parallel execution. Plot
methods for trained and resampled supervised models draw interactive figures through
'rtemis.draw'. Includes native implementations of SuperLearner, RuleFit, and Linear
Additive Trees, and supports conformal prediction. The package first appeared in
E.D. Gennatas (2017)
<https://repository.upenn.edu/entities/publication/d81892ea-3087-4b71-a6f5-739c58626d64>.

Efstathios D. Gennatas

rtemis

Schema-Driven Machine Learning and Visualization

E.D. Gennatas

setup_GLMNET function

<dl><dt>alpha</dt>
<dd>(Tunable) Numeric [0, 1]: Elastic net mixing parameter.</dd>
<dt>family</dt>
<dd>Optional Character {"gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"}: Family. NULL = set from outcome type.</dd>
<dt>which_lambda_cv</dt>
<dd>Character {"lambda.1se", "lambda.min"}: Which lambda to use for prediction.</dd>
<dt>nlambda</dt>
<dd>Integer [1, Inf): Number of lambda values.</dd>
<dt>lambda</dt>
<dd>Optional Numeric [0, Inf) vector: Lambda values. NULL = determined by cv.glmnet during tuning.</dd>
<dt>penalty_factor</dt>
<dd>Optional Numeric [0, Inf) vector: Penalty factor for each design-matrix column (factors expanded to indicator columns). NULL uses 1 for every column.</dd>
<dt>standardize</dt>
<dd>Logical: If TRUE, standardize features.</dd>
<dt>intercept</dt>
<dd>Logical: If TRUE, include intercept.</dd>
<dt>ifw</dt>
<dd>(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in classification.</dd></dl>

Arguments

Author

Setup GLMNET Hyperparameters — setup_GLMNET

<dl>

<dt>alpha</dt>
<dd>(Tunable) Numeric [0, 1]: Elastic net mixing parameter.</dd>


<dt>family</dt>
<dd>Optional Character {"gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"}: Family. NULL = set from outcome type.</dd>


<dt>which_lambda_cv</dt>
<dd>Character {"lambda.1se", "lambda.min"}: Which lambda to use for prediction.</dd>


<dt>nlambda</dt>
<dd>Integer [1, Inf): Number of lambda values.</dd>


<dt>lambda</dt>
<dd>Optional Numeric [0, Inf) vector: Lambda values. NULL = determined by cv.glmnet during tuning.</dd>


<dt>penalty_factor</dt>
<dd>Optional Numeric [0, Inf) vector: Penalty factor for each design-matrix column (factors expanded to indicator columns). NULL uses 1 for every column.</dd>


<dt>standardize</dt>
<dd>Logical: If TRUE, standardize features.</dd>


<dt>intercept</dt>
<dd>Logical: If TRUE, include intercept.</dd>


<dt>ifw</dt>
<dd>(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in classification.</dd>

</dl>

setup_GLMNET: Setup GLMNET Hyperparameters

Description

Usage

Value

Arguments

Author

Details

Examples