Guarded Resampling Workflows for Safe and Automated Machine Learning in R
Plot ICE curves for a fastml model
Plot method for fastml_stability objects
Plot Methods for fastml Objects
Internal predict_model method for parsnip fits
Predict Risk Scores from a Survival Model
Predict survival probabilities from a survival model
Predict method for fastml objects
Print Default Differences Table
Print Tuning Presets Summary
Print method for fastml_stability objects
Process and Evaluate a Model Workflow
Recommend Tuning Configuration
Reset Default Override Warnings
Resolve the positive class for binary classification
Clean Column Names or Character Vectors by Removing Special Characters
Save Model Function
Summary Function for fastml (Using yardstick for ROC Curves)
Fit a surrogate decision tree for a fastml model
Train Specified Machine Learning Algorithms on the Training Data
Tuning Configuration and Complexity Presets
Load Model Function
Map Brier Curve Values to Specific Horizons
Compute Uno's C-index (Time-Dependent AUC)
Convert Various Prediction Formats to Survival Matrix
Get Default Parameters with Transparency Warnings
Align Survival Curve to Evaluation Times
Assign Risk Groups
Get Tuning Parameters for Complexity Level
Get Available Methods
Build Survival Matrix from survfit Object
Clamp Values to [0, 1]
Compare fastml and parsnip defaults
Compute Integrated Brier Score and Curve
Compute Difference in Restricted Mean Survival Time (RMST)
Compute Survival Matrix from survreg Model
Compute Tau Limit (t_max)
Generate counterfactual explanations for a fastml model
Create Censoring Distribution Evaluator
Defaults Registry for Engine and Parameter Transparency
Determine rounding digits for time horizons
Environment for Tracking Warned Defaults
Estimate Tuning Time
Compute Accumulated Local Effects (ALE) for a fastml model
Generate DALEX explanations for a fastml model
Generate LIME explanations for a fastml model
Analyze Feature Importance Stability Across Cross-Validation Folds
Extract survreg Linear Predictor and Scale
Explain a fastml model using various techniques
Lightweight exploratory helper
Evaluate Models Function
Guarded Resampling Utilities
Get Default Parameters for an Algorithm
Internal helpers for survival-specific preprocessing
Internal helper to prepare explainer inputs from a fastml object
Fast Machine Learning Function
Flatten and Rename Models
Format Default Override Warning Message
Get Best Model Indices by Metric and Group
Get Best Model Names
Get Best Workflows
Get All Default Differences Summary
Get Default Engine
Compute feature interaction strengths for a fastml model
Get Default Tuning Parameters
Get Engine Names from Model Workflows
Expanded Default Tuning Parameters
Get Model Engine Names
Get Parsnip Default Engine for an Algorithm
Get Parsnip Default Parameters for an Algorithm
Extract Time and Status from Survival Matrix
Tuning Complexity Presets
Validate Defaults Registry Against Parsnip
Warn About Default Overrides
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.
Useful links