Cross-Validation for Model Selection


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Documentation for package ‘cvms’ version 1.3.0

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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