Package index • ppforest2

All functions

NCI60: NCI60 Dataset
bag_samples(): In-bag row indices per tree.
binarize_disabled(): Disabled binarization strategy (placeholder).
binarize_largest_gap(): Largest-gap binarization strategy.
california_housing: California Housing Dataset
crab: Australian Crabs Dataset
crabs: Crabs Dataset
cutpoint_mean_of_means(): Mean-of-means split cutpoint strategy.
fishcatch: Fish Catch Dataset
fitted(<ppmodel>): Fitted (in-sample) predictions from a ppforest2 model.
formula(<ppmodel>): Formula extractor for ppforest2 models.
glass: Glass Dataset
grouping_by_cutpoint(): Cutpoint-based grouping strategy (regression).
grouping_by_label(): Label-based grouping strategy.
image: Image Dataset
leaf_majority_vote(): Majority-vote leaf strategy.
leaf_mean_response(): Mean-response leaf strategy.
leukemia: Leukemia Dataset
load_json(): Load a model from a JSON file.
lymphoma: Lymphoma Dataset
nobs(<ppmodel>): Number of observations used to fit a ppforest2 model.
olive: Olive Dataset
oob_error(): Out-of-bag error for a random forest.
oob_predictions(): Out-of-bag predictions for a random forest.
oob_samples(): Out-of-bag row indices per tree.
parkinson: Parkinson Dataset
permuted_importance(): Permuted variable importance for a random forest.
plot(<pprf>): Plot a pprf model.
plot(<pptr>): Plot a pptr model.
pp_pda(): PDA projection pursuit strategy.
pp_rand_forest(): Parsnip model specification for pprf.
pp_tree(): Parsnip model specification for pptr.
pprf(): Trains a Random Forest of Projection-Pursuit oblique decision trees.
pptr(): Trains a Projection-Pursuit oblique decision tree.
predict(<pprf_classification>): Predicts labels or vote proportions from a pprf model (classification mode).
predict(<pprf_regression>): Predicts numeric responses from a pprf model (regression mode).
predict(<pptr_classification>): Predicts labels or per-group one-hot proportions from a pptr model (classification mode).
predict(<pptr_regression>): Predicts numeric responses from a pptr model (regression mode).
print(<pprf>): Prints a compact summary of a pprf forest.
print(<pptr>): Prints the structure of a pptr tree.
projection_importance(): Projection-coefficient variable importance.
residuals(<ppmodel>): Residuals from a regression ppforest2 model.
save_json(): Save a model to a JSON file.
stop_any(): Composite stopping rule (logical OR).
stop_max_depth(): Maximum-depth stopping rule.
stop_min_size(): Minimum-size stopping rule.
stop_min_variance(): Minimum-variance stopping rule.
stop_pure_node(): Pure-node stopping rule.
summary(<pprf>): Summary of a pprf forest (shared header + VI).
update(<pp_rand_forest>): Update a pp_rand_forest model specification.
update(<pp_tree>): Update a pp_tree model specification.
vars_all(): All-variables selection strategy.
vars_uniform(): Uniform random variable selection strategy.
weighted_importance(): Weighted projection variable importance for a random forest.
wine: Wine Dataset