predict_risk

predict_risk.fastml_native_survival

predict_risk.workflow

predict_risk.default

Provides a consistent interface for computing linear predictors (risk scores)
across various survival modeling engines, including native fastml models
(e.g., Cox proportional hazards, XGBoost Cox) and parsnip/workflow objects.

internal

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

predict_risk function

<dl><dt>fit</dt>
<dd>A fitted survival model object.</dd>
<dt>newdata</dt>
<dd>A data frame containing predictor variables for which to
compute risk scores.</dd>
<dt>...</dt>
<dd>Additional arguments passed to specific methods.</dd></dl>

Arguments

Predict Risk Scores from a Survival Model — predict_risk

<dl>

<dt>fit</dt>
<dd>A fitted survival model object.</dd>


<dt>newdata</dt>
<dd>A data frame containing predictor variables for which to
compute risk scores.</dd>


<dt>...</dt>
<dd>Additional arguments passed to specific methods.</dd>

</dl>

predict_risk: Predict Risk Scores from a Survival Model

Description

Usage

Value

Arguments