Man pages for jiho/joml
Utilities for Machine Learning
| classification_metrics | Compute classification quality metrics |
| classification_report | Build a classification report |
| confusion_matrix | Build a confusion matrix |
| defactor | Convert a factor into integers using 0-based indexing |
| fit_one_xgb | Fit an xgboost model to _one_ 'resamples' object |
| importance | Compute variable importance for each resample |
| majority_vote | Return the majority vote in a discrete valued vector |
| param_grid | Define a parameter grid, to be explored through the fitting... |
| partials | Compute univariate partial dependence for each resample |
| permute | Create permutations of resamples |
| plot.cm | Plot a confusion matrix |
| plot_importance | Plot variable importance |
| plot_partials | Plot partial dependence plots |
| refactor | Convert a vector of 0-based indexed integers into a factor |
| regression_metrics | Compute regression quality metrics |
| replicate | Replicate each row of a resamples object |
| resample_boot | Generate data resamples using bootstrap |
| resample_cv | Generate data resamples using cross validation |
| resample_identity | Generate repeated resamples of the same data |
| resample_split | Generate train-val splits of the data |
| se | Compute the standard error of the mean, assuming a normal... |
| split_in_folds | Split n items into k folds |
| summarise_importance | Summarise variable importance across resamples |
| summarise_partials | Summarise partial dependence across resamples |
| xgb_fit | Fit an xgboost model for each row of a 'resamples' object |
| xgb_predict | Predict from an xgboost model at a given number of rounds,... |
| xgb_summarise_fit | Summarise the fit of xgboost models over resamples |
