List predictor scoring functions
See also
Other preference_order_tools:
f_auto(),
f_auto_rules()
Examples
f_functions()
#> name response_type predictors_types
#> 1 f_numeric_glm numeric mixed
#> 2 f_numeric_gam numeric numeric
#> 3 f_numeric_rf numeric mixed
#> 4 f_count_rf integer mixed
#> 5 f_count_glm integer mixed
#> 6 f_count_gam integer mixed
#> 7 f_binomial_glm binomial mixed
#> 8 f_binomial_gam binomial numeric
#> 9 f_binomial_rf binomial mixed
#> 10 f_categorical_rf categorical mixed
#> expression
#> 1 stats::glm(y ~ x, family = gaussian(link = 'identity'))
#> 2 mgcv::gam(y ~ s(x), family = gaussian(link = 'identity'))
#> 3 ranger::ranger(y ~ x)
#> 4 ranger::ranger(y ~ x)
#> 5 stats::glm(y ~ x, family = poisson(link = 'log'))
#> 6 mgcv::gam(y ~ s(x), family = poisson(link = 'log'))
#> 7 stats::glm(y ~ x, family = quasibinomial(link = 'logit'), weights = case_weights(y))
#> 8 mgcv::gam(y ~ s(x), family = quasibinomial(link = 'logit'), weights = collinear::case_weights(y))
#> 9 ranger::ranger(y ~ x, case.weights = collinear::case_weights(y))
#> 10 ranger::ranger(y ~ x, case.weights = collinear::case_weights(y))
#> metric
#> 1 R-squared
#> 2 R-squared
#> 3 R-squared
#> 4 R-squared
#> 5 R-squared
#> 6 R-squared
#> 7 AUC
#> 8 AUC
#> 9 AUC
#> 10 Cramer's V