Tidy Characterizations of Model Performance

accuracy | Accuracy |

bal_accuracy | Balanced accuracy |

ccc | Concordance correlation coefficient |

conf_mat | Confusion Matrix for Categorical Data |

detection_prevalence | Detection prevalence |

developer-helpers | Developer helpers |

f_meas | F Measure |

gain_capture | Gain capture |

gain_curve | Gain curve |

hpc_cv | Class Probability Predictions |

j_index | J-index |

kap | Kappa |

lift_curve | Lift curve |

mae | Mean absolute error |

mape | Mean absolute percent error |

mcc | Matthews correlation coefficient |

metrics | General Function to Estimate Performance |

metric_set | Combine metric functions |

metric_summarizer | Developer function for summarizing new metrics |

metric_vec_template | Developer function for calling new metrics |

mn_log_loss | Mean log loss |

npv | Negative predictive value |

pathology | Liver Pathology Data |

ppv | Positive predictive value |

pr_auc | Area under the precision recall curve |

pr_curve | Precision recall curve |

precision | Precision |

recall | Recall |

reexports | Objects exported from other packages |

rmse | Root mean squared error |

roc_auc | Area under the receiver operator curve |

roc_curve | Receiver operator curve |

rpd | Ratio of performance to deviation |

rpiq | Ratio of performance to inter-quartile |

rsq | R squared |

rsq_trad | R squared - traditional |

sens | Sensitivity |

solubility_test | Solubility Predictions from MARS Model |

spec | Specificity |

summary.conf_mat | Summary Statistics for Confusion Matrices |

two_class_example | Two Class Predictions |

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