cvms-package | cvms: A package for cross-validating regression and classification models |

as.character.process_info_binomial | A set of process information object constructors |

as.character.process_info_gaussian | A set of process information object constructors |

as.character.process_info_multinomial | A set of process information object constructors |

baseline | Create baseline evaluations |

baseline_binomial | Create baseline evaluations for binary classification |

baseline_gaussian | Create baseline evaluations for regression models |

baseline_multinomial | Create baseline evaluations |

binomial_metrics | Select metrics for binomial evaluation |

combine_predictors | Generate model formulas by combining predictors |

compatible.formula.terms | Compatible formula terms |

confusion_matrix | Create a confusion matrix |

cross_validate | Cross-validate regression models for model selection |

cross_validate_fn | Cross-validate custom model functions for model selection |

cvms | cvms: A package for cross-validating regression and classification models |

evaluate | Evaluate your model's performance |

evaluate_residuals | Evaluate residuals from a regression task |

font | Create a list of font settings for plots |

gaussian_metrics | Select metrics for Gaussian evaluation |

generate_formulas | Generate model formulas by combining predictors |

hardest | Find the data points that were hardest to predict |

model_functions | Examples of model_fn functions |

most_challenging | Find the data points that were hardest to predict |

multiclass_probability_tibble | Generate a multiclass probability tibble |

multinomial_metrics | Select metrics for multinomial evaluation |

musicians | Musician groups |

participant.scores | Participant scores |

plot_confusion_matrix | Plot a confusion matrix |

plot_metric_density | Density plot for a metric |

precomputed.formulas | Precomputed formulas |

predicted.musicians | Predicted musician groups |

predict_functions | Examples of predict_fn functions |

preprocess_functions | Examples of preprocess_fn functions |

print.process_info_binomial | A set of process information object constructors |

print.process_info_gaussian | A set of process information object constructors |

print.process_info_multinomial | A set of process information object constructors |

process_info_binomial | A set of process information object constructors |

process_info_gaussian | A set of process information object constructors |

process_info_multinomial | A set of process information object constructors |

reconstruct_formulas | Reconstruct model formulas from results tibbles |

select_definitions | Select model definition columns |

select_metrics | Select columns with evaluation metrics and model definitions |

simplify_formula | Simplify formula with inline functions |

summarize_metrics | Summarize metrics with common descriptors |

sum_tile_settings | Create a list of settings for the sum tiles in plot_confusion_matrix() |

update_hyperparameters | Check and update hyperparameters |

validate | Validate regression models on a test set |

validate_fn | Validate a custom model function on a test set |

wines | Wine varieties |