Man pages for talegari/forager
Compute auxiliary information (proximity, dissimilarity, outlyingness, depth) and imputation from tree ensembles on new data
| depth_from_randomForest | depth_from_randomForest |
| depth_from_ranger | depth_from_ranger |
| dist | Distance matrix from unsupervised tree ensemble |
| dist_extract | Extract distances between specified indexes from a 'dist'... |
| distExtractPair | distExtractPair |
| forage | Obtain various outputs using a trained tree ensemble on new... |
| forager | Compute auxiliary information (proximity, dissimilarity,... |
| forest_impute | Impute using a tree ensemble in un/supervised setting |
| forest_rfe | lightweight implementation of RFE using ranger |
| generate_synthetic_data | Generate synthetic data for random forest |
| oob_error | Extract OOB error from a forest model |
| ooberror.randomForest | Extract OOB error from a randomForest model |
| ooberror.ranger | Extract OOB error from a ranger model |
| predict_depth_observations | predict_depth_observations |
| predict_depth_observations.randomForest | predict_depth_observations.randomForest |
| predict_depth_observations.ranger | predict_depth_observations.ranger |
| proximity | Proximity matrix from unsupervised tree ensemble |
| roughImpute | Unsupervised median/mode imputation for dataframes |
| sampler | Sample a categorical or numeric vector using random or... |
| stack_impute | Imputation by stacking complete and incomplete data |
| synthetic_forest | Grow a tree ensemble on synthetic data |
