| treemisc-package | Data Sets and Functions to Accompany "Tree-Based Methods for Statistical Learning in R" |
| banknote | Swiss banknote data |
| banknote2 | Swiss banknote data (UCI version) |
| calibrate | External probability calibration |
| cummean | Cumulative means |
| decision_boundary | Add decision boundary to a scatterplot. |
| decision_boundary.default | Add decision boundary to a scatterplot. |
| gbm_2way | Two-way interactions |
| gen_friedman1 | Friedman benchmark data |
| gen_mease | Generate data from the Mease model |
| guide_setup | Generate GUIDE input files |
| hitters | Baseball data (corrected) |
| isle_post | Importance sampled learning ensemble |
| ladboost | Gradient tree boosting with least absolute deviation (LAD) loss |
| lift | Gain and lift charts |
| load_eslmix | Gaussian mixture data |
| lsboost | Gradient tree boosting with least squares (LS) loss |
| mushroom | Mushroom edibility |
| plot.calibrate | External probability calibration |
| plot.lift | Gain and lift charts |
| predict.ladboost | Gradient tree boosting with least absolute deviation (LAD) loss |
| predict.lsboost | Gradient tree boosting with least squares (LS) loss |
| predict.rforest | Random forest predictions |
| print.calibrate | External probability calibration |
| print.ladboost | Gradient tree boosting with least absolute deviation (LAD) loss |
| print.lsboost | Gradient tree boosting with least squares (LS) loss |
| proximity | Proximity matrix |
| proximity.default | Proximity matrix |
| proximity.matrix | Proximity matrix |
| proximity.ranger | Proximity matrix |
| prune_se | Prune an 'rpart' object |
| rforest | Random forest |
| rrm | Random rotation matrix |
| treemisc | Data Sets and Functions to Accompany "Tree-Based Methods for Statistical Learning in R" |
| tree_diagram | Tree diagram |
| wilson_hilferty | Modified Wilson-Hilferty approximation |
| wine | Wine quality |
| xy_grid | Create a Cartesian product from evenly spaced values of two variables |
| xy_grid.data.frame | Create a Cartesian product from evenly spaced values of two variables |
| xy_grid.default | Create a Cartesian product from evenly spaced values of two variables |
| xy_grid.formula | Create a Cartesian product from evenly spaced values of two variables |
| xy_grid.matrix | Create a Cartesian product from evenly spaced values of two variables |