These functions read and write to the environment where the package stores information about model specifications.
Arguments
- items
A character string of objects in the model environment.
- ...
Named values that will be assigned to the model environment.
- name
A single character value for a new symbol in the model environment.
- value
A single value for a new value in the model environment.
References
"How to build a parsnip model" https://www.tidymodels.org/learn/develop/models/
Examples
# Access the model data:
current_code <- get_model_env()
ls(envir = current_code)
#> [1] "C5_rules" "C5_rules_args"
#> [3] "C5_rules_fit" "C5_rules_modes"
#> [5] "C5_rules_pkgs" "C5_rules_predict"
#> [7] "auto_ml" "auto_ml_args"
#> [9] "auto_ml_fit" "auto_ml_modes"
#> [11] "auto_ml_pkgs" "auto_ml_predict"
#> [13] "bag_mars" "bag_mars_args"
#> [15] "bag_mars_fit" "bag_mars_modes"
#> [17] "bag_mars_pkgs" "bag_mars_predict"
#> [19] "bag_mlp" "bag_mlp_args"
#> [21] "bag_mlp_fit" "bag_mlp_modes"
#> [23] "bag_mlp_pkgs" "bag_mlp_predict"
#> [25] "bag_tree" "bag_tree_args"
#> [27] "bag_tree_fit" "bag_tree_modes"
#> [29] "bag_tree_pkgs" "bag_tree_predict"
#> [31] "bart" "bart_args"
#> [33] "bart_encoding" "bart_fit"
#> [35] "bart_modes" "bart_pkgs"
#> [37] "bart_predict" "boost_tree"
#> [39] "boost_tree_args" "boost_tree_encoding"
#> [41] "boost_tree_fit" "boost_tree_modes"
#> [43] "boost_tree_pkgs" "boost_tree_predict"
#> [45] "cubist_rules" "cubist_rules_args"
#> [47] "cubist_rules_fit" "cubist_rules_modes"
#> [49] "cubist_rules_pkgs" "cubist_rules_predict"
#> [51] "decision_tree" "decision_tree_args"
#> [53] "decision_tree_encoding" "decision_tree_fit"
#> [55] "decision_tree_modes" "decision_tree_pkgs"
#> [57] "decision_tree_predict" "discrim_flexible"
#> [59] "discrim_flexible_args" "discrim_flexible_fit"
#> [61] "discrim_flexible_modes" "discrim_flexible_pkgs"
#> [63] "discrim_flexible_predict" "discrim_linear"
#> [65] "discrim_linear_args" "discrim_linear_fit"
#> [67] "discrim_linear_modes" "discrim_linear_pkgs"
#> [69] "discrim_linear_predict" "discrim_quad"
#> [71] "discrim_quad_args" "discrim_quad_fit"
#> [73] "discrim_quad_modes" "discrim_quad_pkgs"
#> [75] "discrim_quad_predict" "discrim_regularized"
#> [77] "discrim_regularized_args" "discrim_regularized_fit"
#> [79] "discrim_regularized_modes" "discrim_regularized_pkgs"
#> [81] "discrim_regularized_predict" "gen_additive_mod"
#> [83] "gen_additive_mod_args" "gen_additive_mod_encoding"
#> [85] "gen_additive_mod_fit" "gen_additive_mod_modes"
#> [87] "gen_additive_mod_pkgs" "gen_additive_mod_predict"
#> [89] "linear_reg" "linear_reg_args"
#> [91] "linear_reg_encoding" "linear_reg_fit"
#> [93] "linear_reg_modes" "linear_reg_pkgs"
#> [95] "linear_reg_predict" "logistic_reg"
#> [97] "logistic_reg_args" "logistic_reg_encoding"
#> [99] "logistic_reg_fit" "logistic_reg_modes"
#> [101] "logistic_reg_pkgs" "logistic_reg_predict"
#> [103] "mars" "mars_args"
#> [105] "mars_encoding" "mars_fit"
#> [107] "mars_modes" "mars_pkgs"
#> [109] "mars_predict" "mlp"
#> [111] "mlp_args" "mlp_encoding"
#> [113] "mlp_fit" "mlp_modes"
#> [115] "mlp_pkgs" "mlp_predict"
#> [117] "models" "modes"
#> [119] "multinom_reg" "multinom_reg_args"
#> [121] "multinom_reg_encoding" "multinom_reg_fit"
#> [123] "multinom_reg_modes" "multinom_reg_pkgs"
#> [125] "multinom_reg_predict" "naive_Bayes"
#> [127] "naive_Bayes_args" "naive_Bayes_fit"
#> [129] "naive_Bayes_modes" "naive_Bayes_pkgs"
#> [131] "naive_Bayes_predict" "nearest_neighbor"
#> [133] "nearest_neighbor_args" "nearest_neighbor_encoding"
#> [135] "nearest_neighbor_fit" "nearest_neighbor_modes"
#> [137] "nearest_neighbor_pkgs" "nearest_neighbor_predict"
#> [139] "null_model" "null_model_args"
#> [141] "null_model_encoding" "null_model_fit"
#> [143] "null_model_modes" "null_model_pkgs"
#> [145] "null_model_predict" "pls"
#> [147] "pls_args" "pls_fit"
#> [149] "pls_modes" "pls_pkgs"
#> [151] "pls_predict" "poisson_reg"
#> [153] "poisson_reg_args" "poisson_reg_fit"
#> [155] "poisson_reg_modes" "poisson_reg_pkgs"
#> [157] "poisson_reg_predict" "proportional_hazards"
#> [159] "proportional_hazards_args" "proportional_hazards_fit"
#> [161] "proportional_hazards_modes" "proportional_hazards_pkgs"
#> [163] "proportional_hazards_predict" "rand_forest"
#> [165] "rand_forest_args" "rand_forest_encoding"
#> [167] "rand_forest_fit" "rand_forest_modes"
#> [169] "rand_forest_pkgs" "rand_forest_predict"
#> [171] "rule_fit" "rule_fit_args"
#> [173] "rule_fit_fit" "rule_fit_modes"
#> [175] "rule_fit_pkgs" "rule_fit_predict"
#> [177] "surv_reg" "surv_reg_args"
#> [179] "surv_reg_encoding" "surv_reg_fit"
#> [181] "surv_reg_modes" "surv_reg_pkgs"
#> [183] "surv_reg_predict" "survival_reg"
#> [185] "survival_reg_args" "survival_reg_fit"
#> [187] "survival_reg_modes" "survival_reg_pkgs"
#> [189] "survival_reg_predict" "svm_linear"
#> [191] "svm_linear_args" "svm_linear_encoding"
#> [193] "svm_linear_fit" "svm_linear_modes"
#> [195] "svm_linear_pkgs" "svm_linear_predict"
#> [197] "svm_poly" "svm_poly_args"
#> [199] "svm_poly_encoding" "svm_poly_fit"
#> [201] "svm_poly_modes" "svm_poly_pkgs"
#> [203] "svm_poly_predict" "svm_rbf"
#> [205] "svm_rbf_args" "svm_rbf_encoding"
#> [207] "svm_rbf_fit" "svm_rbf_modes"
#> [209] "svm_rbf_pkgs" "svm_rbf_predict"