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topepotopepo
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more model conversions
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‎NAMESPACE

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# Generated by roxygen2: do not edit by hand
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S3method(fit,model_spec)
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S3method(fit_xy,model_spec)
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S3method(multi_predict,"_C5.0")
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S3method(multi_predict,"_earth")
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S3method(multi_predict,"_elnet")
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S3method(multi_predict,"_lognet")
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S3method(multi_predict,"_multnet")
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S3method(multi_predict,"_xgb.Booster")
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S3method(multi_predict,default)
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S3method(nullmodel,default)
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S3method(predict,"_elnet")
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S3method(predict,"_lognet")
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S3method(predict,"_multnet")
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S3method(predict,model_fit)
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S3method(predict,model_spec)
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S3method(predict,nullmodel)
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S3method(predict_class,"_lognet")
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S3method(predict_class,"_multnet")
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S3method(predict_classprob,"_lognet")
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S3method(predict_classprob,"_multnet")
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S3method(predict_numeric,"_elnet")
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S3method(predict_raw,"_elnet")
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S3method(predict_raw,"_lognet")
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S3method(predict_raw,"_multnet")
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S3method(print,boost_tree)
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S3method(print,decision_tree)
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S3method(print,fit_control)
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S3method(print,linear_reg)
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S3method(print,logistic_reg)
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S3method(print,mars)
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S3method(print,mlp)
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S3method(print,model_fit)
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S3method(print,model_spec)
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S3method(print,multinom_reg)
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S3method(print,nearest_neighbor)
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S3method(print,nullmodel)
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S3method(print,rand_forest)
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S3method(print,surv_reg)
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S3method(print,svm_poly)
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S3method(print,svm_rbf)
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S3method(translate,boost_tree)
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S3method(translate,decision_tree)
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S3method(translate,default)
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S3method(translate,mars)
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S3method(translate,mlp)
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S3method(translate,rand_forest)
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S3method(translate,surv_reg)
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S3method(translate,svm_poly)
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S3method(translate,svm_rbf)
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S3method(type_sum,model_fit)
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S3method(type_sum,model_spec)
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S3method(update,boost_tree)
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S3method(update,decision_tree)
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S3method(update,linear_reg)
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S3method(update,logistic_reg)
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S3method(update,mars)
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S3method(update,mlp)
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S3method(update,multinom_reg)
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S3method(update,nearest_neighbor)
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S3method(update,rand_forest)
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S3method(update,surv_reg)
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S3method(update,svm_poly)
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S3method(update,svm_rbf)
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S3method(varying_args,model_spec)
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S3method(varying_args,recipe)
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S3method(varying_args,step)
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export("%>%")
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export(.cols)
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export(.dat)
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export(.facts)
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export(.lvls)
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export(.obs)
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export(.preds)
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export(.x)
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export(.y)
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export(C5.0_train)
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export(add_rowindex)
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export(boost_tree)
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export(check_arg_val)
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export(check_empty_ellipse)
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export(check_engine_val)
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export(check_fit_info)
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export(check_func_val)
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export(check_mod_val)
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export(check_mode_val)
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export(check_pkg_val)
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export(check_pred_info)
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export(check_submodels_val)
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export(decision_tree)
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export(fit)
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export(fit.model_spec)
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export(fit_control)
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export(fit_xy)
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export(fit_xy.model_spec)
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export(get_dependency)
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export(get_fit)
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export(get_model_env)
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export(get_pred_type)
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export(keras_mlp)
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export(linear_reg)
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export(logistic_reg)
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export(make_classes)
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export(mars)
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export(mlp)
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export(model_printer)
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export(multi_predict)
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export(multinom_reg)
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export(nearest_neighbor)
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export(null_model)
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export(nullmodel)
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export(predict.model_fit)
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export(rand_forest)
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export(rpart_train)
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export(set_args)
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export(set_dependency)
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export(set_engine)
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export(set_fit)
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export(set_mode)
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export(set_model_arg)
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export(set_model_engine)
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export(set_model_mode)
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export(set_new_model)
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export(set_pred)
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export(show_call)
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export(show_model_info)
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export(surv_reg)
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export(svm_poly)
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export(svm_rbf)
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export(tidy.model_fit)
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export(translate)
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export(validate_model)
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export(varying)
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export(varying_args)
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export(xgb_train)
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importFrom(dplyr,arrange)
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importFrom(dplyr,as_tibble)
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importFrom(dplyr,bind_cols)

‎R/linear_reg_data.R

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value = list(
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interface = "formula",
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protect = c("x", "formula", "weight_col"),
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func = c(pkg = "sparklyr", fun = "ml_linear_regression")
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func = c(pkg = "sparklyr", fun = "ml_linear_regression"),
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defaults = list()
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)
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)
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set_model_engine("linear_reg", "regression", "keras")
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set_dependency("linear_reg", "keras", c("keras", "magrittr"))
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set_dependency("linear_reg", "keras", "keras")
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set_dependency("linear_reg", "keras", "magrittr")
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set_fit(
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mod = "linear_reg",

‎R/mars_data.R

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mars_arg_key <- data.frame(
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earth = c( "nprune", "degree", "pmethod"),
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stringsAsFactors = FALSE,
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row.names = c("num_terms", "prod_degree", "prune_method")
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)
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mars_modes <- c("classification", "regression", "unknown")
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mars_engines <- data.frame(
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earth = rep(TRUE, 3),
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row.names = c("classification", "regression", "unknown")
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)
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# ------------------------------------------------------------------------------
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set_new_model("mars")
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set_model_mode("mars", "classification")

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