Man pages for probably
Tools for Post-Processing Predicted Values
| append_class_pred | Add a 'class_pred' column |
| as_class_pred | Coerce to a 'class_pred' object |
| boosting_predictions | Boosted regression trees predictions |
| bound_prediction | Truncate a numeric prediction column |
| cal_apply | Applies a calibration to a set of existing predictions |
| cal_binary_tables | Probability Calibration table |
| cal_estimate_beta | Uses a Beta calibration model to calculate new probabilities |
| cal_estimate_isotonic | Uses an Isotonic regression model to calibrate model... |
| cal_estimate_isotonic_boot | Uses a bootstrapped Isotonic regression model to calibrate... |
| cal_estimate_linear | Uses a linear regression model to calibrate numeric... |
| cal_estimate_logistic | Uses a logistic regression model to calibrate probabilities |
| cal_estimate_multinomial | Uses a Multinomial calibration model to calculate new... |
| cal_estimate_none | Do not calibrate model predictions. |
| cal_plot_breaks | Probability calibration plots via binning |
| cal_plot_logistic | Probability calibration plots via logistic regression |
| cal_plot_regression | Regression calibration plots |
| cal_plot_windowed | Probability calibration plots via moving windows |
| cal_validate_beta | Measure performance with and without using Beta calibration |
| cal_validate_isotonic | Measure performance with and without using isotonic... |
| cal_validate_isotonic_boot | Measure performance with and without using bagged isotonic... |
| cal_validate_linear | Measure performance with and without using linear regression... |
| cal_validate_logistic | Measure performance with and without using logistic... |
| cal_validate_multinomial | Measure performance with and without using multinomial... |
| cal_validate_none | Measure performance without using calibration |
| class_pred | Create a class prediction object |
| collect_metrics.cal_rset | Obtain and format metrics produced by calibration validation |
| collect_predictions.cal_rset | Obtain and format predictions produced by calibration... |
| control_conformal_full | Controlling the numeric details for conformal inference |
| int_conformal_cv | Prediction intervals via conformal inference CV+ |
| int_conformal_full | Prediction intervals via conformal inference |
| int_conformal_quantile | Prediction intervals via conformal inference and quantile... |
| int_conformal_split | Prediction intervals via split conformal inference |
| is_class_pred | Test if an object inherits from 'class_pred' |
| levels.class_pred | Extract 'class_pred' levels |
| locate-equivocal | Locate equivocal values |
| make_class_pred | Create a 'class_pred' vector from class probabilities |
| predict.int_conformal_full | Prediction intervals from conformal methods |
| probably-package | probably: Tools for Post-Processing Predicted Values |
| reexports | Objects exported from other packages |
| reportable_rate | Calculate the reportable rate |
| required_pkgs.cal_object | S3 methods to track which additional packages are needed for... |
| segment_naive_bayes | Image segmentation predictions |
| species_probs | Predictions on animal species |
| threshold_perf | Generate performance metrics across probability thresholds |
