Cross-Validation for Model Selection

---

Documentation for package ‘cvms’ version 1.6.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

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