Fast Machine Learning Model Training and Evaluation
Align Survival Curve to Evaluation Times
Assign Risk Groups
Get Available Methods
Build Survival Matrix from survfit Object
Clamp Values to [0, 1]
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)
Compute Uno's C-index (Time-Dependent AUC)
Convert Various Prediction Formats to Survival Matrix
Generate counterfactual explanations for a fastml model
Create Censoring Distribution Evaluator
Determine rounding digits for time horizons
Evaluate Models Function
Compute Accumulated Local Effects (ALE) for a fastml model
Generate DALEX explanations for a fastml model
Generate LIME explanations for a fastml model
Extract survreg Linear Predictor and Scale
Explain a fastml model using various techniques
Explore and Summarize a Dataset Quickly
Internal helpers for survival-specific preprocessing
Fast Machine Learning Function
Flatten and Rename Models
Framingham Heart Study Data
Get Best Model Indices by Metric and Group
Get Best Model Names
Get Best Workflows
Get Default Engine
Get Default Parameters for an Algorithm
Get Default Tuning Parameters
Get Engine Names from Model Workflows
Get Model Engine Names
Extract Time and Status from Survival Matrix
Compute feature interaction strengths for a fastml model
Load Model Function
Map Brier Curve Values to Specific Horizons
Plot ICE curves for a fastml model
Plot Methods for fastml Objects
Predict Risk Scores from a Survival Model
Predict survival probabilities from a survival model
Predict method for fastml objects
Process and Evaluate a Model Workflow
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
Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code by providing comprehensive data preprocessing and support for a wide range of algorithms with hyperparameter tuning. It offers performance metrics and visualization tools to facilitate efficient and effective machine learning workflows.
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