Man pages for ppforest2
Projection Pursuit Oblique Decision Trees and Random Forests
| 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. |
| pp_pda | PDA projection pursuit strategy. |
| pp_rand_forest | Parsnip model specification for pprf. |
| pprf | Trains a Random Forest of Projection-Pursuit oblique decision... |
| pptr | Trains a Projection-Pursuit oblique decision tree. |
| pp_tree | Parsnip model specification for pptr. |
| predict.pprf_classification | Predicts labels or vote proportions from a pprf model... |
| predict.pprf_regression | Predicts numeric responses from a pprf model (regression... |
| predict.pptr_classification | Predicts labels or per-group one-hot proportions from a pptr... |
| predict.pptr_regression | Predicts numeric responses from a pptr model (regression... |
| 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 |
