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Oblique random forests (RFs)

Fit, inspect, summarize, and apply oblique RFs

orsf() orsf_train()
Oblique Random Forests
orsf_update()
Update Forest Parameters
print(<ObliqueForest>)
Inspect Forest Parameters
predict(<ObliqueForest>)
Prediction for ObliqueForest Objects
orsf_summarize_uni()
Univariate summary
print(<orsf_summary_uni>)
Print ORSF summary

Control how your oblique RF works

Choose how to identify linear combinations of predictors and set tuning parameters for your approach

orsf_control() orsf_control_classification() orsf_control_regression() orsf_control_survival()
Oblique random forest control
orsf_control_cph()
Cox regression ORSF control
orsf_control_custom()
Custom ORSF control
orsf_control_fast()
Accelerated ORSF control
orsf_control_net()
Penalized Cox regression ORSF control

Variable importance/selection

Estimate the importance of individual variables and conduct variable selection using ORSFs

orsf_vi() orsf_vi_negate() orsf_vi_permute() orsf_vi_anova()
Variable Importance
orsf_vint()
Variable Interactions
orsf_vs()
Variable selection

Partial dependence and individual conditional expectations

Interpret your model by generating partial dependence or individual conditional expectation values. Plotting functions not included (but see examples)

orsf_ice_oob() orsf_ice_inb() orsf_ice_new()
Individual Conditional Expectations
orsf_pd_oob() orsf_pd_inb() orsf_pd_new()
Partial dependence
pred_spec_auto()
Automatic variable values for dependence

Example survival data

Datasets used in examples and vignettes.

pbc_orsf
Mayo Clinic Primary Biliary Cholangitis Data
penguins_orsf
Size measurements for adult foraging penguins near Palmer Station, Antarctica

Miscellaneous

Functions that don’t fit neatly into a category above, but are still helpful.

as.data.table(<orsf_summary_uni>)
Coerce to data.table
orsf_time_to_train()
Estimate training time

Back-end functions

Techniques used by aorsf that may be helpful in other contexts.