plot_ice

Generates Individual Conditional Expectation (ICE) plots for selected features
using the `pdp` package (ggplot2 engine), and returns both the underlying data
and the plot object.

Provides a guarded resampling workflow for training and evaluating machine‑learning models.
When the guarded resampling path is used, preprocessing and model fitting are re‑estimated within
each resampling split to reduce leakage risk. Supports multiple resampling schemes, integrates
with established engines in the 'tidymodels' ecosystem, and aims to improve evaluation reliability by
coordinating preprocessing, fitting, and evaluation within supported workflows. Offers a lightweight
AutoML‑style workflow by automating model training, resampling, and tuning across multiple algorithms,
while keeping evaluation design explicit and user‑controlled.

Selcuk Korkmaz

fastml

Guarded Resampling Workflows for Safe and Automated Machine
Learning in R

Dincer Goksuluk

Eda Karaismailoglu

plot_ice function

<dl><dt>object</dt>
<dd>A `fastml` object.</dd>
<dt>features</dt>
<dd>Character vector of feature names to plot.</dd>
<dt>...</dt>
<dd>Additional arguments passed to `pdp::partial`.</dd></dl>

Arguments

Plot ICE curves for a fastml model — plot_ice

<dl>

<dt>object</dt>
<dd>A `fastml` object.</dd>


<dt>features</dt>
<dd>Character vector of feature names to plot.</dd>


<dt>...</dt>
<dd>Additional arguments passed to `pdp::partial`.</dd>

</dl>

plot_ice: Plot ICE curves for a fastml model

Description

Usage

Value

Arguments

Examples