tl_predict_regularized

Predict using a regularized regression model

internal

Provides a unified tidyverse-compatible interface to R's machine
learning packages. Wraps established implementations from 'glmnet',
'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster',
'dbscan', and others - providing consistent function signatures, tidy tibble
output, and unified 'ggplot2'-based visualization. The underlying algorithms
are unchanged; 'tidylearn' simply makes them easier to use together. Access
raw model objects via the $fit slot for package-specific functionality.
Methods include random forests Breiman (2001) <doi:10.1023/A:1010933404324>,
LASSO regression Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>,
elastic net Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>,
support vector machines Cortes and Vapnik (1995) <doi:10.1007/BF00994018>,
and gradient boosting Friedman (2001) <doi:10.1214/aos/1013203451>.

Cesaire Tobias

tidylearn

A Unified Tidy Interface to R's Machine Learning Ecosystem

tl_predict_regularized function

<dl><dt>model</dt>
<dd>A tidylearn regularized model object</dd>
<dt>new_data</dt>
<dd>A data frame containing the new data</dd>
<dt>type</dt>
<dd>Type of prediction: "response" (default), "class" (for classification), "prob" (for classification)</dd>
<dt>lambda</dt>
<dd>Which lambda to use for prediction ("1se" or "min", default: "1se")</dd>
<dt>...</dt>
<dd>Additional arguments</dd></dl>

Arguments

Predict using a regularized regression model — tl_predict_regularized

<dl>

<dt>model</dt>
<dd>A tidylearn regularized model object</dd>


<dt>new_data</dt>
<dd>A data frame containing the new data</dd>


<dt>type</dt>
<dd>Type of prediction: "response" (default), "class" (for classification), "prob" (for classification)</dd>


<dt>lambda</dt>
<dd>Which lambda to use for prediction ("1se" or "min", default: "1se")</dd>


<dt>...</dt>
<dd>Additional arguments</dd>

</dl>

tl_predict_regularized: Predict using a regularized regression model

Description

Usage

Value

Arguments