Package: ggRandomForests 4.0.0

What varPro is | Regression: Boston housing | Per-tree importance with gg_varpro() | Partial dependence with gg_partial_varpro() | Per-rule lasso refinement with gg_beta_varpro() | Unsupervised views: see the uvarpro vignette | Anomaly scoring with gg_isopro() | Local importance with gg_ivarpro() | Classification: iris | Class-conditional importance with gg_varpro(conditional = TRUE) | Partial dependence: gg_partial_varpro() on classification | Per-class lasso refinement with gg_beta_varpro() | Survival: PBC | Variable importance: gg_varpro() | Partial dependence: gg_partial_varpro() on survival | Anomaly scoring: gg_isopro() on the X-matrix | Not available for survival: gg_beta_varpro, gg_ivarpro | Small or sparse data | Cross-cutting reference | Family-support matrix | Which variables can you actually get? | Factor-level ordering | Caching the expensive calls | Provenance shape | Further reading | References

The data | Which variables did varPro test? | What does a z of zero mean? | Reading the importance plot | How much do the settings matter? | Checking against noise | A screen or a selection? | Partial dependence on small data | References

What does this variable do? | The data, and a forest | What the data alone shows | Averaging over everything else: partial dependence | The neighborhood that doesn't exist | How different are they, really? | Where two variables act together | One prediction at a time | Local by construction | Two estimands that are easy to confuse | Which one do you reach for? | What is not here | References

Work in progress | Introduction | Data: Primary Biliary Cirrhosis (PBC) | Data cleaning | Exploratory data analysis | Kaplan--Meier survival by treatment | Growing a Random Survival Forest | OOB error convergence | OOB predicted survival | Test set predictions | Variable Selection | Variable importance (VIMP) | Minimal depth | Variable Dependence | Variable dependence plots | Partial dependence | Conditional dependence | Partial Dependence Surfaces | Brier Score and CRPS | Conclusion | References

Work in progress | Introduction | Data: Boston Housing Values | Exploratory data analysis | Growing a Random Forest | OOB error convergence | OOB predictions | Variable Selection | Variable importance (VIMP) | Minimal depth | SHAP Analysis | SHAP importance | SHAP beeswarm | SHAP dependence | Variable Dependence | Variable dependence plots | Partial dependence | Variable Interactions and Conditioning Plots | Conditioning on a categorical variable | Conditioning on a continuous variable | Partial Dependence Surface | Conclusion | References

Error trajectories with gg_error() | Marginal dependence via gg_variable() | Variable importance with gg_vimp() | Balanced conditioning cuts with quantile_pts() | Next steps

Work in progress | Introduction | Data: Iris Flower Measurements | Exploratory data analysis | Growing a Random Forest | OOB error convergence | OOB predictions | Variable Selection | Variable importance (VIMP) | Minimal depth | SHAP Analysis | SHAP importance | SHAP beeswarm | SHAP dependence | Variable Dependence | Variable dependence plots | Partial dependence | Classification Performance: ROC and AUC | Conclusion | References

What changes with counting-process data? | Where did the fitted objects come from? | How do hazard and cumulative hazard differ? | Hazard is local | Why do the hazard curves stop early? | Cumulative hazard adds the local rates | How does discrimination change over time? | Cumulative/dynamic AUC uses cumulative hazard | Incident/dynamic AUC uses hazard | Which variables matter, and when? | Which tree size did the upstream searches select? | OOB risk tuning | OOB iAUC tuning | What does each extractor return? | How can this analysis be reproduced? | Further reading

Importance without a response | One fit, three views | What depends on what: gg_udependent() | What ranks highest: gg_beta_uvarpro() | Where to draw the line: gg_sdependent() | Reading the three together | References