From f77694654ff8f59ec266cc27cd96b2f07e968d26 Mon Sep 17 00:00:00 2001 From: akhilabburu <76109555+akhilabburu@users.noreply.github.com> Date: Wed, 3 Sep 2025 12:29:08 +0530 Subject: [PATCH 1/3] Fixed minor spelling error --- R/translate.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/translate.R b/R/translate.R index cb5815c98..fb6b020ba 100644 --- a/R/translate.R +++ b/R/translate.R @@ -30,13 +30,13 @@ #' @examplesIf !parsnip:::is_cran_check() #' lm_spec <- linear_reg(penalty = 0.01) #' -#' # `penalty` is tranlsated to `lambda` +#' # `penalty` is translated to `lambda` #' translate(lm_spec, engine = "glmnet") #' #' # `penalty` not applicable for this model. #' translate(lm_spec, engine = "lm") #' -#' # `penalty` is tranlsated to `reg_param` +#' # `penalty` is translated to `reg_param` #' translate(lm_spec, engine = "spark") #' #' # with a placeholder for an unknown argument value: From f15f310bf0a3fe684888d6e1072bd37f755043bc Mon Sep 17 00:00:00 2001 From: hfrick Date: Wed, 3 Sep 2025 08:57:51 +0000 Subject: [PATCH 2/3] Document --- man/details_C5_rules_C5.0.Rd | 2 +- man/details_auto_ml_h2o.Rd | 2 +- man/details_boost_tree_h2o.Rd | 2 +- man/details_boost_tree_lightgbm.Rd | 2 +- man/details_cubist_rules_Cubist.Rd | 2 +- man/details_linear_reg_h2o.Rd | 2 +- man/details_logistic_reg_h2o.Rd | 2 +- man/details_mlp_h2o.Rd | 2 +- man/details_multinom_reg_h2o.Rd | 2 +- man/details_naive_Bayes_h2o.Rd | 2 +- man/details_poisson_reg_h2o.Rd | 2 +- man/details_rand_forest_h2o.Rd | 2 +- man/details_rule_fit_h2o.Rd | 6 +++--- man/details_rule_fit_xrf.Rd | 2 +- man/translate.Rd | 4 ++-- 15 files changed, 18 insertions(+), 18 deletions(-) diff --git a/man/details_C5_rules_C5.0.Rd b/man/details_C5_rules_C5.0.Rd index 9708e75d3..c35397532 100644 --- a/man/details_C5_rules_C5.0.Rd +++ b/man/details_C5_rules_C5.0.Rd @@ -5,7 +5,7 @@ \title{C5.0 rule-based classification models} \description{ \code{\link[C50:C5.0]{C50::C5.0()}} fits a model that derives feature rules from a tree for -prediction. A single tree or boosted ensemble can be used. \code{\link[rules:rules-internal]{rules::c5_fit()}} +prediction. A single tree or boosted ensemble can be used. \code{\link[rules:c5_fit]{rules::c5_fit()}} is a wrapper around this function. } \details{ diff --git a/man/details_auto_ml_h2o.Rd b/man/details_auto_ml_h2o.Rd index e94bb222d..a2fc5c30e 100644 --- a/man/details_auto_ml_h2o.Rd +++ b/man/details_auto_ml_h2o.Rd @@ -29,7 +29,7 @@ performance assessment and potential early stopping. \subsection{Translation from parsnip to the original package (regression)}{ -\code{\link[agua:h2o_train]{agua::h2o_train_auto()}} is a wrapper around +\code{\link[agua:h2o_train_auto]{agua::h2o_train_auto()}} is a wrapper around \code{\link[h2o:h2o.automl]{h2o::h2o.automl()}}. \if{html}{\out{
}}\preformatted{auto_ml() |> diff --git a/man/details_boost_tree_h2o.Rd b/man/details_boost_tree_h2o.Rd index 1d1233c4e..056527550 100644 --- a/man/details_boost_tree_h2o.Rd +++ b/man/details_boost_tree_h2o.Rd @@ -42,7 +42,7 @@ the metric and early stopping criteria on the validation set. \subsection{Translation from parsnip to the original package (regression)}{ -\code{\link[agua:h2o_train]{agua::h2o_train_xgboost()}} is a wrapper +\code{\link[agua:h2o_train_xgboost]{agua::h2o_train_xgboost()}} is a wrapper around \code{\link[h2o:h2o.xgboost]{h2o::h2o.xgboost()}}. The \strong{agua} extension package is required to fit this model. diff --git a/man/details_boost_tree_lightgbm.Rd b/man/details_boost_tree_lightgbm.Rd index f9bfee657..85cf9b26a 100644 --- a/man/details_boost_tree_lightgbm.Rd +++ b/man/details_boost_tree_lightgbm.Rd @@ -175,7 +175,7 @@ that the booster will perform bagging at every \code{k}th boosting iteration. Thus, by default, the \code{sample_size} argument would be ignored without setting this argument manually. Other boosting libraries, like xgboost, do not have an analogous argument to \code{bagging_freq} and use \code{k = 1} when -the analogue to \code{bagging_fraction} is in $\verb{(0, 1)}$. \emph{bonsai will thus +the analogue to \code{bagging_fraction} is in $(0, 1)$. \emph{bonsai will thus automatically set} \code{bagging_freq = 1} \emph{in} \code{set_engine("lightgbm", ...)} if \code{sample_size} (i.e. \code{bagging_fraction}) is not equal to 1 and no \code{bagging_freq} value is supplied. This default can be overridden by diff --git a/man/details_cubist_rules_Cubist.Rd b/man/details_cubist_rules_Cubist.Rd index 72d295a40..acc186860 100644 --- a/man/details_cubist_rules_Cubist.Rd +++ b/man/details_cubist_rules_Cubist.Rd @@ -6,7 +6,7 @@ \description{ \code{\link[Cubist:cubist.default]{Cubist::cubist()}} fits a model that derives simple feature rules from a tree ensemble and uses creates regression models within each rule. -\code{\link[rules:rules-internal]{rules::cubist_fit()}} is a wrapper around this function. +\code{\link[rules:cubist_fit]{rules::cubist_fit()}} is a wrapper around this function. } \details{ For this engine, there is a single mode: regression diff --git a/man/details_linear_reg_h2o.Rd b/man/details_linear_reg_h2o.Rd index a53190643..daabeaf4a 100644 --- a/man/details_linear_reg_h2o.Rd +++ b/man/details_linear_reg_h2o.Rd @@ -32,7 +32,7 @@ defaults to 0 (ridge regression) and 0.5 otherwise. \subsection{Translation from parsnip to the original package}{ -\code{\link[agua:h2o_train]{agua::h2o_train_glm()}} for \code{linear_reg()} is a +\code{\link[agua:h2o_train_glm]{agua::h2o_train_glm()}} for \code{linear_reg()} is a wrapper around \code{\link[h2o:h2o.glm]{h2o::h2o.glm()}} with \code{family = "gaussian"}. diff --git a/man/details_logistic_reg_h2o.Rd b/man/details_logistic_reg_h2o.Rd index e88d8d03d..329c04fa9 100644 --- a/man/details_logistic_reg_h2o.Rd +++ b/man/details_logistic_reg_h2o.Rd @@ -34,7 +34,7 @@ defaults to 0 (ridge regression) and 0.5 otherwise. \subsection{Translation from parsnip to the original package}{ -\code{\link[agua:h2o_train]{agua::h2o_train_glm()}} for \code{logistic_reg()} is +\code{\link[agua:h2o_train_glm]{agua::h2o_train_glm()}} for \code{logistic_reg()} is a wrapper around \code{\link[h2o:h2o.glm]{h2o::h2o.glm()}}. h2o will automatically picks the link function and distribution family or binomial responses. diff --git a/man/details_mlp_h2o.Rd b/man/details_mlp_h2o.Rd index 9b82c34f1..a0ed86f55 100644 --- a/man/details_mlp_h2o.Rd +++ b/man/details_mlp_h2o.Rd @@ -52,7 +52,7 @@ in the input layer, which defaults to 0. \subsection{Translation from parsnip to the original package (regression)}{ -\link[agua:h2o_train]{agua::h2o_train_mlp} is a wrapper around +\link[agua:h2o_train_mlp]{agua::h2o_train_mlp} is a wrapper around \code{\link[h2o:h2o.deeplearning]{h2o::h2o.deeplearning()}}. \if{html}{\out{
}}\preformatted{mlp( diff --git a/man/details_multinom_reg_h2o.Rd b/man/details_multinom_reg_h2o.Rd index 6d3a434a8..f99608b3f 100644 --- a/man/details_multinom_reg_h2o.Rd +++ b/man/details_multinom_reg_h2o.Rd @@ -33,7 +33,7 @@ defaults to 0 (ridge regression) and 0.5 otherwise. \subsection{Translation from parsnip to the original package}{ -\code{\link[agua:h2o_train]{agua::h2o_train_glm()}} for \code{multinom_reg()} is +\code{\link[agua:h2o_train_glm]{agua::h2o_train_glm()}} for \code{multinom_reg()} is a wrapper around \code{\link[h2o:h2o.glm]{h2o::h2o.glm()}} with \code{family = 'multinomial'}. diff --git a/man/details_naive_Bayes_h2o.Rd b/man/details_naive_Bayes_h2o.Rd index a7e6fb18a..04f44352b 100644 --- a/man/details_naive_Bayes_h2o.Rd +++ b/man/details_naive_Bayes_h2o.Rd @@ -36,7 +36,7 @@ enough data. The \strong{agua} extension package is required to fit this model. -\code{\link[agua:h2o_train]{agua::h2o_train_nb()}} is a wrapper around +\code{\link[agua:h2o_train_nb]{agua::h2o_train_nb()}} is a wrapper around \code{\link[h2o:h2o.naiveBayes]{h2o::h2o.naiveBayes()}}. \if{html}{\out{
}}\preformatted{naive_Bayes(Laplace = numeric(0)) |> diff --git a/man/details_poisson_reg_h2o.Rd b/man/details_poisson_reg_h2o.Rd index c69b3f722..d49d74c7b 100644 --- a/man/details_poisson_reg_h2o.Rd +++ b/man/details_poisson_reg_h2o.Rd @@ -33,7 +33,7 @@ defaults to 0 (ridge regression) and 0.5 otherwise. \subsection{Translation from parsnip to the original package}{ -\code{\link[agua:h2o_train]{agua::h2o_train_glm()}} for \code{poisson_reg()} is +\code{\link[agua:h2o_train_glm]{agua::h2o_train_glm()}} for \code{poisson_reg()} is a wrapper around \code{\link[h2o:h2o.glm]{h2o::h2o.glm()}} with \code{family = 'poisson'}. diff --git a/man/details_rand_forest_h2o.Rd b/man/details_rand_forest_h2o.Rd index ec02e468d..506f04739 100644 --- a/man/details_rand_forest_h2o.Rd +++ b/man/details_rand_forest_h2o.Rd @@ -28,7 +28,7 @@ regression. \subsection{Translation from parsnip to the original package (regression)}{ -\code{\link[agua:h2o_train]{agua::h2o_train_rf()}} is a wrapper around +\code{\link[agua:h2o_train_rf]{agua::h2o_train_rf()}} is a wrapper around \code{\link[h2o:h2o.randomForest]{h2o::h2o.randomForest()}}. \if{html}{\out{
}}\preformatted{rand_forest( diff --git a/man/details_rule_fit_h2o.Rd b/man/details_rule_fit_h2o.Rd index e029b4852..9e6a81e8b 100644 --- a/man/details_rule_fit_h2o.Rd +++ b/man/details_rule_fit_h2o.Rd @@ -5,7 +5,7 @@ \title{RuleFit models via h2o} \description{ \code{\link[h2o:h2o.rulefit]{h2o::h2o.rulefit()}} fits a model that derives simple feature rules from a tree -ensemble and uses the rules as features to a regularized (LASSO) model. \code{\link[agua:h2o_train]{agua::h2o_train_rule()}} +ensemble and uses the rules as features to a regularized (LASSO) model. \code{\link[agua:h2o_train_rule]{agua::h2o_train_rule()}} is a wrapper around this function. } \details{ @@ -38,7 +38,7 @@ of: “rules_and_linear”, “rules”, “linear”, defaults to \subsection{Translation from parsnip to the underlying model call (regression)}{ -\code{\link[agua:h2o_train]{agua::h2o_train_rule()}} is a wrapper around +\code{\link[agua:h2o_train_rule]{agua::h2o_train_rule()}} is a wrapper around \code{\link[h2o:h2o.rulefit]{h2o::h2o.rulefit()}}. The \strong{agua} extension package is required to fit this model. @@ -73,7 +73,7 @@ rule_fit( \subsection{Translation from parsnip to the underlying model call (classification)}{ -\code{\link[agua:h2o_train]{agua::h2o_train_rule()}} for \code{rule_fit()} is a +\code{\link[agua:h2o_train_rule]{agua::h2o_train_rule()}} for \code{rule_fit()} is a wrapper around \code{\link[h2o:h2o.rulefit]{h2o::h2o.rulefit()}}. The \strong{agua} extension package is required to fit this model. diff --git a/man/details_rule_fit_xrf.Rd b/man/details_rule_fit_xrf.Rd index 29e941abb..abb5ddce1 100644 --- a/man/details_rule_fit_xrf.Rd +++ b/man/details_rule_fit_xrf.Rd @@ -5,7 +5,7 @@ \title{RuleFit models via xrf} \description{ \code{\link[xrf:xrf]{xrf::xrf()}} fits a model that derives simple feature rules from a tree -ensemble and uses the rules as features to a regularized model. \code{\link[rules:rules-internal]{rules::xrf_fit()}} +ensemble and uses the rules as features to a regularized model. \code{\link[rules:xrf_fit]{rules::xrf_fit()}} is a wrapper around this function. } \details{ diff --git a/man/translate.Rd b/man/translate.Rd index b181fe49e..072486d6f 100644 --- a/man/translate.Rd +++ b/man/translate.Rd @@ -45,13 +45,13 @@ to modify the model specification. \dontshow{if (!parsnip:::is_cran_check()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} lm_spec <- linear_reg(penalty = 0.01) -# `penalty` is tranlsated to `lambda` +# `penalty` is translated to `lambda` translate(lm_spec, engine = "glmnet") # `penalty` not applicable for this model. translate(lm_spec, engine = "lm") -# `penalty` is tranlsated to `reg_param` +# `penalty` is translated to `reg_param` translate(lm_spec, engine = "spark") # with a placeholder for an unknown argument value: From 6e90a07e2debb03b0db0cc42f5f222d8cad5f2ee Mon Sep 17 00:00:00 2001 From: Hannah Frick Date: Wed, 3 Sep 2025 10:10:23 +0100 Subject: [PATCH 3/3] document --- man/details_C5_rules_C5.0.Rd | 2 +- man/details_auto_ml_h2o.Rd | 2 +- man/details_boost_tree_h2o.Rd | 2 +- man/details_boost_tree_lightgbm.Rd | 2 +- man/details_cubist_rules_Cubist.Rd | 2 +- man/details_linear_reg_h2o.Rd | 2 +- man/details_logistic_reg_h2o.Rd | 2 +- man/details_mlp_h2o.Rd | 2 +- man/details_multinom_reg_h2o.Rd | 2 +- man/details_naive_Bayes_h2o.Rd | 2 +- man/details_poisson_reg_h2o.Rd | 2 +- man/details_rand_forest_h2o.Rd | 2 +- man/details_rule_fit_h2o.Rd | 6 +++--- man/details_rule_fit_xrf.Rd | 2 +- 14 files changed, 16 insertions(+), 16 deletions(-) diff --git a/man/details_C5_rules_C5.0.Rd b/man/details_C5_rules_C5.0.Rd index c35397532..9708e75d3 100644 --- a/man/details_C5_rules_C5.0.Rd +++ b/man/details_C5_rules_C5.0.Rd @@ -5,7 +5,7 @@ \title{C5.0 rule-based classification models} \description{ \code{\link[C50:C5.0]{C50::C5.0()}} fits a model that derives feature rules from a tree for -prediction. A single tree or boosted ensemble can be used. \code{\link[rules:c5_fit]{rules::c5_fit()}} +prediction. A single tree or boosted ensemble can be used. \code{\link[rules:rules-internal]{rules::c5_fit()}} is a wrapper around this function. } \details{ diff --git a/man/details_auto_ml_h2o.Rd b/man/details_auto_ml_h2o.Rd index a2fc5c30e..e94bb222d 100644 --- a/man/details_auto_ml_h2o.Rd +++ b/man/details_auto_ml_h2o.Rd @@ -29,7 +29,7 @@ performance assessment and potential early stopping. \subsection{Translation from parsnip to the original package (regression)}{ -\code{\link[agua:h2o_train_auto]{agua::h2o_train_auto()}} is a wrapper around +\code{\link[agua:h2o_train]{agua::h2o_train_auto()}} is a wrapper around \code{\link[h2o:h2o.automl]{h2o::h2o.automl()}}. \if{html}{\out{
}}\preformatted{auto_ml() |> diff --git a/man/details_boost_tree_h2o.Rd b/man/details_boost_tree_h2o.Rd index 056527550..1d1233c4e 100644 --- a/man/details_boost_tree_h2o.Rd +++ b/man/details_boost_tree_h2o.Rd @@ -42,7 +42,7 @@ the metric and early stopping criteria on the validation set. \subsection{Translation from parsnip to the original package (regression)}{ -\code{\link[agua:h2o_train_xgboost]{agua::h2o_train_xgboost()}} is a wrapper +\code{\link[agua:h2o_train]{agua::h2o_train_xgboost()}} is a wrapper around \code{\link[h2o:h2o.xgboost]{h2o::h2o.xgboost()}}. The \strong{agua} extension package is required to fit this model. diff --git a/man/details_boost_tree_lightgbm.Rd b/man/details_boost_tree_lightgbm.Rd index 85cf9b26a..f9bfee657 100644 --- a/man/details_boost_tree_lightgbm.Rd +++ b/man/details_boost_tree_lightgbm.Rd @@ -175,7 +175,7 @@ that the booster will perform bagging at every \code{k}th boosting iteration. Thus, by default, the \code{sample_size} argument would be ignored without setting this argument manually. Other boosting libraries, like xgboost, do not have an analogous argument to \code{bagging_freq} and use \code{k = 1} when -the analogue to \code{bagging_fraction} is in $(0, 1)$. \emph{bonsai will thus +the analogue to \code{bagging_fraction} is in $\verb{(0, 1)}$. \emph{bonsai will thus automatically set} \code{bagging_freq = 1} \emph{in} \code{set_engine("lightgbm", ...)} if \code{sample_size} (i.e. \code{bagging_fraction}) is not equal to 1 and no \code{bagging_freq} value is supplied. This default can be overridden by diff --git a/man/details_cubist_rules_Cubist.Rd b/man/details_cubist_rules_Cubist.Rd index acc186860..72d295a40 100644 --- a/man/details_cubist_rules_Cubist.Rd +++ b/man/details_cubist_rules_Cubist.Rd @@ -6,7 +6,7 @@ \description{ \code{\link[Cubist:cubist.default]{Cubist::cubist()}} fits a model that derives simple feature rules from a tree ensemble and uses creates regression models within each rule. -\code{\link[rules:cubist_fit]{rules::cubist_fit()}} is a wrapper around this function. +\code{\link[rules:rules-internal]{rules::cubist_fit()}} is a wrapper around this function. } \details{ For this engine, there is a single mode: regression diff --git a/man/details_linear_reg_h2o.Rd b/man/details_linear_reg_h2o.Rd index daabeaf4a..a53190643 100644 --- a/man/details_linear_reg_h2o.Rd +++ b/man/details_linear_reg_h2o.Rd @@ -32,7 +32,7 @@ defaults to 0 (ridge regression) and 0.5 otherwise. \subsection{Translation from parsnip to the original package}{ -\code{\link[agua:h2o_train_glm]{agua::h2o_train_glm()}} for \code{linear_reg()} is a +\code{\link[agua:h2o_train]{agua::h2o_train_glm()}} for \code{linear_reg()} is a wrapper around \code{\link[h2o:h2o.glm]{h2o::h2o.glm()}} with \code{family = "gaussian"}. diff --git a/man/details_logistic_reg_h2o.Rd b/man/details_logistic_reg_h2o.Rd index 329c04fa9..e88d8d03d 100644 --- a/man/details_logistic_reg_h2o.Rd +++ b/man/details_logistic_reg_h2o.Rd @@ -34,7 +34,7 @@ defaults to 0 (ridge regression) and 0.5 otherwise. \subsection{Translation from parsnip to the original package}{ -\code{\link[agua:h2o_train_glm]{agua::h2o_train_glm()}} for \code{logistic_reg()} is +\code{\link[agua:h2o_train]{agua::h2o_train_glm()}} for \code{logistic_reg()} is a wrapper around \code{\link[h2o:h2o.glm]{h2o::h2o.glm()}}. h2o will automatically picks the link function and distribution family or binomial responses. diff --git a/man/details_mlp_h2o.Rd b/man/details_mlp_h2o.Rd index a0ed86f55..9b82c34f1 100644 --- a/man/details_mlp_h2o.Rd +++ b/man/details_mlp_h2o.Rd @@ -52,7 +52,7 @@ in the input layer, which defaults to 0. \subsection{Translation from parsnip to the original package (regression)}{ -\link[agua:h2o_train_mlp]{agua::h2o_train_mlp} is a wrapper around +\link[agua:h2o_train]{agua::h2o_train_mlp} is a wrapper around \code{\link[h2o:h2o.deeplearning]{h2o::h2o.deeplearning()}}. \if{html}{\out{
}}\preformatted{mlp( diff --git a/man/details_multinom_reg_h2o.Rd b/man/details_multinom_reg_h2o.Rd index f99608b3f..6d3a434a8 100644 --- a/man/details_multinom_reg_h2o.Rd +++ b/man/details_multinom_reg_h2o.Rd @@ -33,7 +33,7 @@ defaults to 0 (ridge regression) and 0.5 otherwise. \subsection{Translation from parsnip to the original package}{ -\code{\link[agua:h2o_train_glm]{agua::h2o_train_glm()}} for \code{multinom_reg()} is +\code{\link[agua:h2o_train]{agua::h2o_train_glm()}} for \code{multinom_reg()} is a wrapper around \code{\link[h2o:h2o.glm]{h2o::h2o.glm()}} with \code{family = 'multinomial'}. diff --git a/man/details_naive_Bayes_h2o.Rd b/man/details_naive_Bayes_h2o.Rd index 04f44352b..a7e6fb18a 100644 --- a/man/details_naive_Bayes_h2o.Rd +++ b/man/details_naive_Bayes_h2o.Rd @@ -36,7 +36,7 @@ enough data. The \strong{agua} extension package is required to fit this model. -\code{\link[agua:h2o_train_nb]{agua::h2o_train_nb()}} is a wrapper around +\code{\link[agua:h2o_train]{agua::h2o_train_nb()}} is a wrapper around \code{\link[h2o:h2o.naiveBayes]{h2o::h2o.naiveBayes()}}. \if{html}{\out{
}}\preformatted{naive_Bayes(Laplace = numeric(0)) |> diff --git a/man/details_poisson_reg_h2o.Rd b/man/details_poisson_reg_h2o.Rd index d49d74c7b..c69b3f722 100644 --- a/man/details_poisson_reg_h2o.Rd +++ b/man/details_poisson_reg_h2o.Rd @@ -33,7 +33,7 @@ defaults to 0 (ridge regression) and 0.5 otherwise. \subsection{Translation from parsnip to the original package}{ -\code{\link[agua:h2o_train_glm]{agua::h2o_train_glm()}} for \code{poisson_reg()} is +\code{\link[agua:h2o_train]{agua::h2o_train_glm()}} for \code{poisson_reg()} is a wrapper around \code{\link[h2o:h2o.glm]{h2o::h2o.glm()}} with \code{family = 'poisson'}. diff --git a/man/details_rand_forest_h2o.Rd b/man/details_rand_forest_h2o.Rd index 506f04739..ec02e468d 100644 --- a/man/details_rand_forest_h2o.Rd +++ b/man/details_rand_forest_h2o.Rd @@ -28,7 +28,7 @@ regression. \subsection{Translation from parsnip to the original package (regression)}{ -\code{\link[agua:h2o_train_rf]{agua::h2o_train_rf()}} is a wrapper around +\code{\link[agua:h2o_train]{agua::h2o_train_rf()}} is a wrapper around \code{\link[h2o:h2o.randomForest]{h2o::h2o.randomForest()}}. \if{html}{\out{
}}\preformatted{rand_forest( diff --git a/man/details_rule_fit_h2o.Rd b/man/details_rule_fit_h2o.Rd index 9e6a81e8b..e029b4852 100644 --- a/man/details_rule_fit_h2o.Rd +++ b/man/details_rule_fit_h2o.Rd @@ -5,7 +5,7 @@ \title{RuleFit models via h2o} \description{ \code{\link[h2o:h2o.rulefit]{h2o::h2o.rulefit()}} fits a model that derives simple feature rules from a tree -ensemble and uses the rules as features to a regularized (LASSO) model. \code{\link[agua:h2o_train_rule]{agua::h2o_train_rule()}} +ensemble and uses the rules as features to a regularized (LASSO) model. \code{\link[agua:h2o_train]{agua::h2o_train_rule()}} is a wrapper around this function. } \details{ @@ -38,7 +38,7 @@ of: “rules_and_linear”, “rules”, “linear”, defaults to \subsection{Translation from parsnip to the underlying model call (regression)}{ -\code{\link[agua:h2o_train_rule]{agua::h2o_train_rule()}} is a wrapper around +\code{\link[agua:h2o_train]{agua::h2o_train_rule()}} is a wrapper around \code{\link[h2o:h2o.rulefit]{h2o::h2o.rulefit()}}. The \strong{agua} extension package is required to fit this model. @@ -73,7 +73,7 @@ rule_fit( \subsection{Translation from parsnip to the underlying model call (classification)}{ -\code{\link[agua:h2o_train_rule]{agua::h2o_train_rule()}} for \code{rule_fit()} is a +\code{\link[agua:h2o_train]{agua::h2o_train_rule()}} for \code{rule_fit()} is a wrapper around \code{\link[h2o:h2o.rulefit]{h2o::h2o.rulefit()}}. The \strong{agua} extension package is required to fit this model. diff --git a/man/details_rule_fit_xrf.Rd b/man/details_rule_fit_xrf.Rd index abb5ddce1..29e941abb 100644 --- a/man/details_rule_fit_xrf.Rd +++ b/man/details_rule_fit_xrf.Rd @@ -5,7 +5,7 @@ \title{RuleFit models via xrf} \description{ \code{\link[xrf:xrf]{xrf::xrf()}} fits a model that derives simple feature rules from a tree -ensemble and uses the rules as features to a regularized model. \code{\link[rules:xrf_fit]{rules::xrf_fit()}} +ensemble and uses the rules as features to a regularized model. \code{\link[rules:rules-internal]{rules::xrf_fit()}} is a wrapper around this function. } \details{