.extract_glmnet_multiclass

internal

It parses a fitted 'R' model object, and returns a formula in
'Tidy Eval' code that calculates the predictions.  It works with
several databases back-ends because it leverages 'dplyr' and 'dbplyr'
for the final 'SQL' translation of the algorithm. It currently
supports lm(), glm(), randomForest(), ranger(), rpart(), earth(),
xgb.Booster.complete(), lgb.Booster(), catboost.Model(), cubist(), and
ctree() models.

Emil Hvitfeldt

tidypredict

Run Predictions Inside the Database

Edgar Ruiz

Max Kuhn

.extract_glmnet_multiclass function

<dl><dt>model</dt>
<dd>A glmnet model object with class "multnet"</dd>
<dt>penalty</dt>
<dd>The penalty value to use for coefficient extraction</dd></dl>

Arguments

Extract multiclass linear predictors for glmnet models — .extract_glmnet_multiclass

<dl>

<dt>model</dt>
<dd>A glmnet model object with class "multnet"</dd>


<dt>penalty</dt>
<dd>The penalty value to use for coefficient extraction</dd>

</dl>

.extract_glmnet_multiclass: Extract multiclass linear predictors for glmnet models

Description

Usage

Arguments