Projection Pursuit Oblique Decision Trees and Random Forests

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Documentation for package ‘ppforest2’ version 0.1.1

• DESCRIPTION file
• User guides, package vignettes and other documentation
• Package NEWS
• Package README

Help Pages

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
NCI60	NCI60 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.
pprf	Trains a Random Forest of Projection-Pursuit oblique decision trees.
pptr	Trains a Projection-Pursuit oblique decision tree.
pp_pda	PDA projection pursuit strategy.
pp_rand_forest	Parsnip model specification for pprf.
pp_tree	Parsnip model specification for pptr.
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.
save_json.ppmodel	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