A B C D F G H I J K L M N P R S T V
| accuracy | Accuracy |
| accuracy.data.frame | Accuracy |
| accuracy_vec | Accuracy |
| average_precision | Area under the precision recall curve |
| average_precision.data.frame | Area under the precision recall curve |
| average_precision_vec | Area under the precision recall curve |
| bal_accuracy | Balanced accuracy |
| bal_accuracy.data.frame | Balanced accuracy |
| bal_accuracy_vec | Balanced accuracy |
| ccc | Concordance correlation coefficient |
| ccc.data.frame | Concordance correlation coefficient |
| ccc_vec | Concordance correlation coefficient |
| classification_cost | Costs function for poor classification |
| classification_cost.data.frame | Costs function for poor classification |
| classification_cost_vec | Costs function for poor classification |
| conf_mat | Confusion Matrix for Categorical Data |
| conf_mat.data.frame | Confusion Matrix for Categorical Data |
| conf_mat.default | Confusion Matrix for Categorical Data |
| conf_mat.table | Confusion Matrix for Categorical Data |
| detection_prevalence | Detection prevalence |
| detection_prevalence.data.frame | Detection prevalence |
| detection_prevalence_vec | Detection prevalence |
| developer-helpers | Developer helpers |
| dots_to_estimate | Developer helpers |
| finalize_estimator | Developer helpers |
| finalize_estimator_internal | Developer helpers |
| f_meas | F Measure |
| f_meas.data.frame | F Measure |
| f_meas_vec | F Measure |
| gain_capture | Gain capture |
| gain_capture.data.frame | Gain capture |
| gain_capture_vec | Gain capture |
| gain_curve | Gain curve |
| gain_curve.data.frame | Gain curve |
| get_weights | Developer helpers |
| hpc_cv | Multiclass Probability Predictions |
| huber_loss | Huber loss |
| huber_loss.data.frame | Huber loss |
| huber_loss_pseudo | Psuedo-Huber Loss |
| huber_loss_pseudo.data.frame | Psuedo-Huber Loss |
| huber_loss_pseudo_vec | Psuedo-Huber Loss |
| huber_loss_vec | Huber loss |
| iic | Index of ideality of correlation |
| iic.data.frame | Index of ideality of correlation |
| iic_vec | Index of ideality of correlation |
| j_index | J-index |
| j_index.data.frame | J-index |
| j_index_vec | J-index |
| kap | Kappa |
| kap.data.frame | Kappa |
| kap_vec | Kappa |
| lift_curve | Lift curve |
| lift_curve.data.frame | Lift curve |
| mae | Mean absolute error |
| mae.data.frame | Mean absolute error |
| mae_vec | Mean absolute error |
| mape | Mean absolute percent error |
| mape.data.frame | Mean absolute percent error |
| mape_vec | Mean absolute percent error |
| mase | Mean absolute scaled error |
| mase.data.frame | Mean absolute scaled error |
| mase_vec | Mean absolute scaled error |
| mcc | Matthews correlation coefficient |
| mcc.data.frame | Matthews correlation coefficient |
| mcc_vec | Matthews correlation coefficient |
| metrics | General Function to Estimate Performance |
| metrics.data.frame | General Function to Estimate Performance |
| metric_set | Combine metric functions |
| metric_summarizer | Developer function for summarizing new metrics |
| metric_tweak | Tweak a metric function |
| metric_vec_template | Developer function for calling new metrics |
| mn_log_loss | Mean log loss for multinomial data |
| mn_log_loss.data.frame | Mean log loss for multinomial data |
| mn_log_loss_vec | Mean log loss for multinomial data |
| mpe | Mean percentage error |
| mpe.data.frame | Mean percentage error |
| mpe_vec | Mean percentage error |
| msd | Mean signed deviation |
| msd.data.frame | Mean signed deviation |
| msd_vec | Mean signed deviation |
| new-metric | Construct a new metric function |
| new_class_metric | Construct a new metric function |
| new_numeric_metric | Construct a new metric function |
| new_prob_metric | Construct a new metric function |
| npv | Negative predictive value |
| npv.data.frame | Negative predictive value |
| npv_vec | Negative predictive value |
| pathology | Liver Pathology Data |
| poisson_log_loss | Mean log loss for Poisson data |
| poisson_log_loss.data.frame | Mean log loss for Poisson data |
| poisson_log_loss_vec | Mean log loss for Poisson data |
| ppv | Positive predictive value |
| ppv.data.frame | Positive predictive value |
| ppv_vec | Positive predictive value |
| precision | Precision |
| precision.data.frame | Precision |
| precision_vec | Precision |
| pr_auc | Area under the precision recall curve |
| pr_auc.data.frame | Area under the precision recall curve |
| pr_auc_vec | Area under the precision recall curve |
| pr_curve | Precision recall curve |
| pr_curve.data.frame | Precision recall curve |
| recall | Recall |
| recall.data.frame | Recall |
| recall_vec | Recall |
| rmse | Root mean squared error |
| rmse.data.frame | Root mean squared error |
| rmse_vec | Root mean squared error |
| roc_auc | Area under the receiver operator curve |
| roc_auc.data.frame | Area under the receiver operator curve |
| roc_auc_vec | Area under the receiver operator curve |
| roc_aunp | Area under the ROC curve of each class against the rest, using the a priori class distribution |
| roc_aunp.data.frame | Area under the ROC curve of each class against the rest, using the a priori class distribution |
| roc_aunp_vec | Area under the ROC curve of each class against the rest, using the a priori class distribution |
| roc_aunu | Area under the ROC curve of each class against the rest, using the uniform class distribution |
| roc_aunu.data.frame | Area under the ROC curve of each class against the rest, using the uniform class distribution |
| roc_aunu_vec | Area under the ROC curve of each class against the rest, using the uniform class distribution |
| roc_curve | Receiver operator curve |
| roc_curve.data.frame | Receiver operator curve |
| rpd | Ratio of performance to deviation |
| rpd.data.frame | Ratio of performance to deviation |
| rpd_vec | Ratio of performance to deviation |
| rpiq | Ratio of performance to inter-quartile |
| rpiq.data.frame | Ratio of performance to inter-quartile |
| rpiq_vec | Ratio of performance to inter-quartile |
| rsq | R squared |
| rsq.data.frame | R squared |
| rsq_trad | R squared - traditional |
| rsq_trad.data.frame | R squared - traditional |
| rsq_trad_vec | R squared - traditional |
| rsq_vec | R squared |
| sens | Sensitivity |
| sens.data.frame | Sensitivity |
| sensitivity | Sensitivity |
| sensitivity.data.frame | Sensitivity |
| sensitivity_vec | Sensitivity |
| sens_vec | Sensitivity |
| smape | Symmetric mean absolute percentage error |
| smape.data.frame | Symmetric mean absolute percentage error |
| smape_vec | Symmetric mean absolute percentage error |
| solubility_test | Solubility Predictions from MARS Model |
| spec | Specificity |
| spec.data.frame | Specificity |
| specificity | Specificity |
| specificity.data.frame | Specificity |
| specificity_vec | Specificity |
| spec_vec | Specificity |
| summary.conf_mat | Summary Statistics for Confusion Matrices |
| tidy.conf_mat | Confusion Matrix for Categorical Data |
| two_class_example | Two Class Predictions |
| validate_estimator | Developer helpers |