Package index • pipeML

Main

Machine learning

compute_features.training.ML(): Train machine learning or survival models with optional stacking and custom cross-validation
compute_features.ML(): Train and evaluate machine learning models for classification or survival analysis
compute_prediction(): Compute Prediction Metrics for a Trained Machine Learning Model

Variable importance

Model interpretability

compute_shap_values(): Compute SHAP Values for Machine Learning Models
plot_shap_stability(): Plot SHAP Feature Importance Stability Across Resamples
feature.selection.boruta(): Compute Feature Selection Using Repeated Boruta Algorithm

Visualization

Model visualization

get_curves(): Get performance curves
plot_survival_performance(): Plot and Save Survival Performance of a Model (Internal)

Package Data

Example data

data_example_classification: data_example_classification
data_example_survival: data_example_survival
counts_example: counts_example
coldata_example: coldata_example