Package: yardstick 1.3.1.9000
Emil Hvitfeldt
yardstick: Tidy Characterizations of Model Performance
Tidy tools for quantifying how well model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE).
Authors:
yardstick_1.3.1.9000.tar.gz
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yardstick.pdf |yardstick.html✨
yardstick/json (API)
NEWS
| # Install 'yardstick' in R: |
| install.packages('yardstick', repos = c('https://tidymodels.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/tidymodels/yardstick/issues
- hpc_cv - Multiclass Probability Predictions
- lung_surv - Survival Analysis Results
- pathology - Liver Pathology Data
- solubility_test - Solubility Predictions from MARS Model
- two_class_example - Two Class Predictions
Last updated 13 days agofrom:8ab89037d1. Checks:OK: 7 NOTE: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 30 2024 |
| R-4.5-win-x86_64 | NOTE | Oct 30 2024 |
| R-4.5-linux-x86_64 | NOTE | Oct 30 2024 |
| R-4.4-win-x86_64 | OK | Oct 30 2024 |
| R-4.4-mac-x86_64 | OK | Oct 30 2024 |
| R-4.4-mac-aarch64 | OK | Oct 30 2024 |
| R-4.3-win-x86_64 | OK | Oct 30 2024 |
| R-4.3-mac-x86_64 | OK | Oct 30 2024 |
| R-4.3-mac-aarch64 | OK | Oct 30 2024 |
Exports:accuracyaccuracy_vecaverage_precisionaverage_precision_vecbal_accuracybal_accuracy_vecbrier_classbrier_class_vecbrier_survivalbrier_survival_integratedbrier_survival_integrated_vecbrier_survival_veccccccc_veccheck_class_metriccheck_dynamic_survival_metriccheck_numeric_metriccheck_prob_metriccheck_static_survival_metricclass_metric_summarizerclassification_costclassification_cost_vecconcordance_survivalconcordance_survival_vecconf_matcurve_metric_summarizercurve_survival_metric_summarizerdemographic_paritydetection_prevalencedetection_prevalence_vecdots_to_estimatedynamic_survival_metric_summarizerequal_opportunityequalized_oddsf_measf_meas_vecfinalize_estimatorfinalize_estimator_internalgain_capturegain_capture_vecgain_curveget_weightshuber_losshuber_loss_pseudohuber_loss_pseudo_vechuber_loss_veciiciic_vecj_indexj_index_veckapkap_veclift_curvemaemae_vecmapemape_vecmasemase_vecmccmcc_vecmetric_setmetric_summarizermetric_tweakmetric_vec_templatemetricsmn_log_lossmn_log_loss_vecmpempe_vecmsdmsd_vecnew_class_metricnew_dynamic_survival_metricnew_groupwise_metricnew_integrated_survival_metricnew_numeric_metricnew_prob_metricnew_static_survival_metricnpvnpv_vecnumeric_metric_summarizerpoisson_log_losspoisson_log_loss_vecppvppv_vecpr_aucpr_auc_vecpr_curveprecisionprecision_vecprob_metric_summarizerrecallrecall_vecrmsermse_vecroc_aucroc_auc_survivalroc_auc_survival_vecroc_auc_vecroc_aunproc_aunp_vecroc_aunuroc_aunu_vecroc_curveroc_curve_survivalrpdrpd_vecrpiqrpiq_vecrsqrsq_tradrsq_trad_vecrsq_vecsenssens_vecsensitivitysensitivity_vecsmapesmape_vecspecspec_vecspecificityspecificity_vecstatic_survival_metric_summarizertidyvalidate_estimatoryardstick_any_missingyardstick_remove_missing
Dependencies:clidplyrfansigenericsgluehardhatlifecyclemagrittrpillarpkgconfigR6rlangsparsevctrstibbletidyselectutf8vctrswithr
Grouping behavior in yardstick
Rendered fromgrouping.Rmdusingknitr::rmarkdownon Oct 30 2024.Last update: 2023-11-02
Started: 2023-11-02
Metric types
Rendered frommetric-types.Rmdusingknitr::rmarkdownon Oct 30 2024.Last update: 2024-04-02
Started: 2018-11-01
Multiclass averaging
Rendered frommulticlass.Rmdusingknitr::rmarkdownon Oct 30 2024.Last update: 2022-11-23
Started: 2018-11-01
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Accuracy | accuracy accuracy.data.frame accuracy_vec |
| Area under the precision recall curve | average_precision average_precision.data.frame average_precision_vec |
| Balanced accuracy | bal_accuracy bal_accuracy.data.frame bal_accuracy_vec |
| Brier score for classification models | brier_class brier_class.data.frame brier_class_vec |
| Time-Dependent Brier score for right censored data | brier_survival brier_survival.data.frame brier_survival_vec |
| Integrated Brier score for right censored data | brier_survival_integrated brier_survival_integrated.data.frame brier_survival_integrated_vec |
| Concordance correlation coefficient | ccc ccc.data.frame ccc_vec |
| Developer function for checking inputs in new metrics | check_class_metric check_dynamic_survival_metric check_metric check_numeric_metric check_prob_metric check_static_survival_metric |
| Costs function for poor classification | classification_cost classification_cost.data.frame classification_cost_vec |
| Concordance index for right-censored data | concordance_survival concordance_survival.data.frame concordance_survival_vec |
| Confusion Matrix for Categorical Data | conf_mat conf_mat.data.frame conf_mat.default conf_mat.table tidy.conf_mat |
| Demographic parity | demographic_parity |
| Detection prevalence | detection_prevalence detection_prevalence.data.frame detection_prevalence_vec |
| Developer helpers | developer-helpers dots_to_estimate finalize_estimator finalize_estimator_internal get_weights validate_estimator |
| Equal opportunity | equal_opportunity |
| Equalized odds | equalized_odds |
| F Measure | f_meas f_meas.data.frame f_meas_vec |
| Gain capture | gain_capture gain_capture.data.frame gain_capture_vec |
| Gain curve | gain_curve gain_curve.data.frame |
| Multiclass Probability Predictions | hpc_cv |
| Huber loss | huber_loss huber_loss.data.frame huber_loss_vec |
| Psuedo-Huber Loss | huber_loss_pseudo huber_loss_pseudo.data.frame huber_loss_pseudo_vec |
| Index of ideality of correlation | iic iic.data.frame iic_vec |
| J-index | j_index j_index.data.frame j_index_vec |
| Kappa | kap kap.data.frame kap_vec |
| Lift curve | lift_curve lift_curve.data.frame |
| Survival Analysis Results | lung_surv |
| Mean absolute error | mae mae.data.frame mae_vec |
| Mean absolute percent error | mape mape.data.frame mape_vec |
| Mean absolute scaled error | mase mase.data.frame mase_vec |
| Matthews correlation coefficient | mcc mcc.data.frame mcc_vec |
| Combine metric functions | metric_set |
| Tweak a metric function | metric_tweak |
| Developer function for summarizing new metrics | class_metric_summarizer curve_metric_summarizer curve_survival_metric_summarizer dynamic_survival_metric_summarizer metric-summarizers numeric_metric_summarizer prob_metric_summarizer static_survival_metric_summarizer |
| General Function to Estimate Performance | metrics metrics.data.frame |
| Mean log loss for multinomial data | mn_log_loss mn_log_loss.data.frame mn_log_loss_vec |
| Mean percentage error | mpe mpe.data.frame mpe_vec |
| Mean signed deviation | msd msd.data.frame msd_vec |
| Create groupwise metrics | new_groupwise_metric |
| Construct a new metric function | new-metric new_class_metric new_dynamic_survival_metric new_integrated_survival_metric new_numeric_metric new_prob_metric new_static_survival_metric |
| Negative predictive value | npv npv.data.frame npv_vec |
| Liver Pathology Data | pathology |
| Mean log loss for Poisson data | poisson_log_loss poisson_log_loss.data.frame poisson_log_loss_vec |
| Positive predictive value | ppv ppv.data.frame ppv_vec |
| Area under the precision recall curve | pr_auc pr_auc.data.frame pr_auc_vec |
| Precision recall curve | pr_curve pr_curve.data.frame |
| Precision | precision precision.data.frame precision_vec |
| Recall | recall recall.data.frame recall_vec |
| Root mean squared error | rmse rmse.data.frame rmse_vec |
| Area under the receiver operator curve | roc_auc roc_auc.data.frame roc_auc_vec |
| Time-Dependent ROC AUC for Censored Data | roc_auc_survival roc_auc_survival.data.frame roc_auc_survival_vec |
| Area under the ROC curve of each class against the rest, using the a priori class distribution | roc_aunp roc_aunp.data.frame roc_aunp_vec |
| Area under the ROC curve of each class against the rest, using the uniform class distribution | roc_aunu roc_aunu.data.frame roc_aunu_vec |
| Receiver operator curve | roc_curve roc_curve.data.frame |
| Time-Dependent ROC surve for Censored Data | roc_curve_survival roc_curve_survival.data.frame |
| Ratio of performance to deviation | rpd rpd.data.frame rpd_vec |
| Ratio of performance to inter-quartile | rpiq rpiq.data.frame rpiq_vec |
| R squared | rsq rsq.data.frame rsq_vec |
| R squared - traditional | rsq_trad rsq_trad.data.frame rsq_trad_vec |
| Sensitivity | sens sens.data.frame sensitivity sensitivity.data.frame sensitivity_vec sens_vec |
| Symmetric mean absolute percentage error | smape smape.data.frame smape_vec |
| Solubility Predictions from MARS Model | solubility_test |
| Specificity | spec spec.data.frame specificity specificity.data.frame specificity_vec spec_vec |
| Summary Statistics for Confusion Matrices | summary.conf_mat |
| Two Class Predictions | two_class_example |
| Developer function for handling missing values in new metrics | yardstick_any_missing yardstick_remove_missing |
