diff --git a/NAMESPACE b/NAMESPACE
index 8f388d332..3bd93bfeb 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -110,6 +110,7 @@ export(fit_control)
 export(fit_xy)
 export(fit_xy.model_spec)
 export(get_dependency)
+export(get_encoding)
 export(get_fit)
 export(get_from_env)
 export(get_model_env)
@@ -146,6 +147,7 @@ export(repair_call)
 export(rpart_train)
 export(set_args)
 export(set_dependency)
+export(set_encoding)
 export(set_engine)
 export(set_env_val)
 export(set_fit)
diff --git a/R/aaa_models.R b/R/aaa_models.R
index ad9235d8e..65b74d3f7 100644
--- a/R/aaa_models.R
+++ b/R/aaa_models.R
@@ -323,6 +323,11 @@ check_interface_val <- function(x) {
 #'  below, depending on context.
 #' @param pre,post Optional functions for pre- and post-processing of prediction
 #'  results.
+#' @param options A list of options for engine-specific encodings. Currently,
+#' the option implemented is `predictor_indicators` which tells `parsnip`
+#' whether the pre-processing should make indicator/dummy variables from factor
+#' predictors. This only affects cases when [fit.model_spec()] is used and the
+#' underlying model has an x/y interface.
 #' @param ... Optional arguments that should be passed into the `args` slot for
 #'  prediction objects.
 #' @keywords internal
@@ -780,3 +785,77 @@ pred_value_template <-  function(pre = NULL, post = NULL, func, ...) {
   list(pre = pre, post = post, func = func, args = list(...))
 }
 
+# ------------------------------------------------------------------------------
+
+check_encodings <- function(x) {
+  if (!is.list(x)) {
+    rlang::abort("`values` should be a list.")
+  }
+  req_args <- list(predictor_indicators = TRUE)
+
+  missing_args <- setdiff(names(req_args), names(x))
+  if (length(missing_args) > 0) {
+    rlang::abort(
+      glue::glue(
+        "The values passed to `set_encoding()` are missing arguments: ",
+        paste0("'", missing_args, "'", collapse = ", ")
+      )
+    )
+  }
+  extra_args <- setdiff(names(x), names(req_args))
+  if (length(extra_args) > 0) {
+    rlang::abort(
+      glue::glue(
+        "The values passed to `set_encoding()` had extra arguments: ",
+        paste0("'", extra_args, "'", collapse = ", ")
+      )
+    )
+  }
+  invisible(x)
+}
+
+#' @export
+#' @rdname set_new_model
+#' @keywords internal
+set_encoding <- function(model, mode, eng, options) {
+  check_model_exists(model)
+  check_eng_val(eng)
+  check_mode_val(mode)
+  check_encodings(options)
+
+  keys   <- tibble::tibble(model = model, engine = eng, mode = mode)
+  options <- tibble::as_tibble(options)
+  new_values <- dplyr::bind_cols(keys, options)
+
+
+  current_db_list <- ls(envir = get_model_env())
+  nm <- paste(model, "encoding", sep = "_")
+  if (any(current_db_list == nm)) {
+    current <- get_from_env(nm)
+    dup_check <-
+      current %>%
+      dplyr::inner_join(new_values, by = c("model", "engine", "mode", "predictor_indicators"))
+    if (nrow(dup_check)) {
+      rlang::abort(glue::glue("Engine '{eng}' and mode '{mode}' already have defined encodings."))
+    }
+
+  } else {
+    current <- NULL
+  }
+
+  db_values <- dplyr::bind_rows(current, new_values)
+  set_env_val(nm, db_values)
+
+  invisible(NULL)
+}
+
+
+#' @rdname set_new_model
+#' @keywords internal
+#' @export
+get_encoding <- function(model) {
+  check_model_exists(model)
+  nm <- paste0(model, "_encoding")
+  rlang::env_get(get_model_env(), nm)
+}
+
diff --git a/R/boost_tree_data.R b/R/boost_tree_data.R
index 25924454d..368b1cba5 100644
--- a/R/boost_tree_data.R
+++ b/R/boost_tree_data.R
@@ -87,6 +87,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "boost_tree",
+  eng = "xgboost",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "boost_tree",
   eng = "xgboost",
@@ -125,6 +132,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "boost_tree",
+  eng = "xgboost",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "boost_tree",
   eng = "xgboost",
@@ -221,6 +235,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "boost_tree",
+  eng = "C5.0",
+  mode = "classification",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_pred(
   model = "boost_tree",
   eng = "C5.0",
@@ -344,6 +365,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "boost_tree",
+  eng = "spark",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_fit(
   model = "boost_tree",
   eng = "spark",
@@ -357,6 +385,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "boost_tree",
+  eng = "spark",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "boost_tree",
   eng = "spark",
diff --git a/R/convert_data.R b/R/convert_data.R
index 6496a1ce9..fe02afce4 100644
--- a/R/convert_data.R
+++ b/R/convert_data.R
@@ -15,7 +15,7 @@
 #' @importFrom stats .checkMFClasses .getXlevels delete.response
 #' @importFrom stats model.offset model.weights na.omit na.pass
 
-convert_form_to_xy_fit <-function(
+convert_form_to_xy_fit <- function(
   formula,
   data,
   ...,
diff --git a/R/decision_tree_data.R b/R/decision_tree_data.R
index 4f0d46160..9eaf8bc69 100644
--- a/R/decision_tree_data.R
+++ b/R/decision_tree_data.R
@@ -48,6 +48,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "decision_tree",
+  eng = "rpart",
+  mode = "regression",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_fit(
   model = "decision_tree",
   eng = "rpart",
@@ -60,6 +67,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "decision_tree",
+  eng = "rpart",
+  mode = "classification",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_pred(
   model = "decision_tree",
   eng = "rpart",
@@ -158,6 +172,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "decision_tree",
+  eng = "C5.0",
+  mode = "classification",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_pred(
   model = "decision_tree",
   eng = "C5.0",
@@ -211,7 +232,7 @@ set_pred(
 
 set_model_engine("decision_tree", "classification", "spark")
 set_model_engine("decision_tree", "regression", "spark")
-set_dependency("decision_tree", "spark", "spark")
+set_dependency("decision_tree", "spark", "sparklyr")
 
 set_model_arg(
   model = "decision_tree",
@@ -239,12 +260,19 @@ set_fit(
     interface = "formula",
     data = c(formula = "formula", data = "x"),
     protect = c("x", "formula"),
-    func = c(pkg = "sparklyr", fun = "ml_decision_tree_classifier"),
+    func = c(pkg = "sparklyr", fun = "ml_decision_tree_regressor"),
     defaults =
       list(seed = expr(sample.int(10 ^ 5, 1)))
   )
 )
 
+set_encoding(
+  model = "decision_tree",
+  eng = "spark",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_fit(
   model = "decision_tree",
   eng = "spark",
@@ -259,6 +287,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "decision_tree",
+  eng = "spark",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "decision_tree",
   eng = "spark",
diff --git a/R/fit.R b/R/fit.R
index 0967026cc..6b1acc10f 100644
--- a/R/fit.R
+++ b/R/fit.R
@@ -103,7 +103,7 @@ fit.model_spec <-
       eng_vals <- possible_engines(object)
       object$engine <- eng_vals[1]
       if (control$verbosity > 0) {
-        rlang::warn("Engine set to `{object$engine}`.")
+        rlang::warn(glue::glue("Engine set to `{object$engine}`."))
       }
     }
 
diff --git a/R/fit_helpers.R b/R/fit_helpers.R
index 49a069cf8..f6940460e 100644
--- a/R/fit_helpers.R
+++ b/R/fit_helpers.R
@@ -103,12 +103,17 @@ xy_xy <- function(object, env, control, target = "none", ...) {
 form_xy <- function(object, control, env,
                     target = "none", ...) {
 
+  indicators <- get_encoding(class(object)[1]) %>%
+    dplyr::filter(mode == object$mode,
+                  engine == object$engine) %>%
+    dplyr::pull(predictor_indicators)
+
   data_obj <- convert_form_to_xy_fit(
     formula = env$formula,
     data = env$data,
     ...,
-    composition = target
-    # indicators
+    composition = target,
+    indicators = indicators
   )
   env$x <- data_obj$x
   env$y <- data_obj$y
diff --git a/R/linear_reg_data.R b/R/linear_reg_data.R
index b5460e66e..2e33efe13 100644
--- a/R/linear_reg_data.R
+++ b/R/linear_reg_data.R
@@ -19,6 +19,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "linear_reg",
+  eng = "lm",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "linear_reg",
   eng = "lm",
@@ -102,6 +109,25 @@ set_pred(
 set_model_engine("linear_reg", "regression", "glmnet")
 set_dependency("linear_reg", "glmnet", "glmnet")
 
+set_fit(
+  model = "linear_reg",
+  eng = "glmnet",
+  mode = "regression",
+  value = list(
+    interface = "matrix",
+    protect = c("x", "y", "weights"),
+    func = c(pkg = "glmnet", fun = "glmnet"),
+    defaults = list(family = "gaussian")
+  )
+)
+
+set_encoding(
+  model = "linear_reg",
+  eng = "glmnet",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_model_arg(
   model = "linear_reg",
   eng = "glmnet",
@@ -120,18 +146,6 @@ set_model_arg(
   has_submodel = FALSE
 )
 
-set_fit(
-  model = "linear_reg",
-  eng = "glmnet",
-  mode = "regression",
-  value = list(
-    interface = "matrix",
-    protect = c("x", "y", "weights"),
-    func = c(pkg = "glmnet", fun = "glmnet"),
-    defaults = list(family = "gaussian")
-  )
-)
-
 set_pred(
   model = "linear_reg",
   eng = "glmnet",
@@ -183,6 +197,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "linear_reg",
+  eng = "stan",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "linear_reg",
   eng = "stan",
@@ -287,6 +308,26 @@ set_pred(
 set_model_engine("linear_reg", "regression", "spark")
 set_dependency("linear_reg", "spark", "sparklyr")
 
+set_fit(
+  model = "linear_reg",
+  eng = "spark",
+  mode = "regression",
+  value = list(
+    interface = "formula",
+    data = c(formula = "formula", data = "x"),
+    protect = c("x", "formula", "weight_col"),
+    func = c(pkg = "sparklyr", fun = "ml_linear_regression"),
+    defaults = list()
+  )
+)
+
+set_encoding(
+  model = "linear_reg",
+  eng = "spark",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_model_arg(
   model = "linear_reg",
   eng = "spark",
@@ -305,20 +346,6 @@ set_model_arg(
   has_submodel = FALSE
 )
 
-
-set_fit(
-  model = "linear_reg",
-  eng = "spark",
-  mode = "regression",
-  value = list(
-    interface = "formula",
-    data = c(formula = "formula", data = "x"),
-    protect = c("x", "formula", "weight_col"),
-    func = c(pkg = "sparklyr", fun = "ml_linear_regression"),
-    defaults = list()
-  )
-)
-
 set_pred(
   model = "linear_reg",
   eng = "spark",
@@ -343,15 +370,6 @@ set_model_engine("linear_reg", "regression", "keras")
 set_dependency("linear_reg", "keras", "keras")
 set_dependency("linear_reg", "keras", "magrittr")
 
-set_model_arg(
-  model = "linear_reg",
-  eng = "keras",
-  parsnip = "penalty",
-  original = "penalty",
-  func = list(pkg = "dials", fun = "penalty"),
-  has_submodel = FALSE
-)
-
 set_fit(
   model = "linear_reg",
   eng = "keras",
@@ -364,6 +382,22 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "linear_reg",
+  eng = "keras",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
+set_model_arg(
+  model = "linear_reg",
+  eng = "keras",
+  parsnip = "penalty",
+  original = "penalty",
+  func = list(pkg = "dials", fun = "penalty"),
+  has_submodel = FALSE
+)
+
 set_pred(
   model = "linear_reg",
   eng = "keras",
diff --git a/R/logistic_reg_data.R b/R/logistic_reg_data.R
index 306bae8b6..0aa4b3e74 100644
--- a/R/logistic_reg_data.R
+++ b/R/logistic_reg_data.R
@@ -19,6 +19,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "logistic_reg",
+  eng = "glm",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "logistic_reg",
   eng = "glm",
@@ -121,6 +128,25 @@ set_pred(
 set_model_engine("logistic_reg", "classification", "glmnet")
 set_dependency("logistic_reg", "glmnet", "glmnet")
 
+set_fit(
+  model = "logistic_reg",
+  eng = "glmnet",
+  mode = "classification",
+  value = list(
+    interface = "matrix",
+    protect = c("x", "y", "weights"),
+    func = c(pkg = "glmnet", fun = "glmnet"),
+    defaults = list(family = "binomial")
+  )
+)
+
+set_encoding(
+  model = "logistic_reg",
+  eng = "glmnet",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_model_arg(
   model = "logistic_reg",
   eng = "glmnet",
@@ -139,19 +165,6 @@ set_model_arg(
   has_submodel = FALSE
 )
 
-set_fit(
-  model = "logistic_reg",
-  eng = "glmnet",
-  mode = "classification",
-  value = list(
-    interface = "matrix",
-    protect = c("x", "y", "weights"),
-    func = c(pkg = "glmnet", fun = "glmnet"),
-    defaults = list(family = "binomial")
-  )
-)
-
-
 set_pred(
   model = "logistic_reg",
   eng = "glmnet",
@@ -246,6 +259,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "logistic_reg",
+  eng = "spark",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "logistic_reg",
   eng = "spark",
@@ -307,6 +327,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "logistic_reg",
+  eng = "keras",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "logistic_reg",
   eng = "keras",
@@ -364,6 +391,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "logistic_reg",
+  eng = "stan",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "logistic_reg",
   eng = "stan",
diff --git a/R/mars_data.R b/R/mars_data.R
index 7ec4b363d..401f04cb7 100644
--- a/R/mars_data.R
+++ b/R/mars_data.R
@@ -47,6 +47,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "mars",
+  eng = "earth",
+  mode = "regression",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_fit(
   model = "mars",
   eng = "earth",
@@ -59,6 +66,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "mars",
+  eng = "earth",
+  mode = "classification",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_pred(
   model = "mars",
   eng = "earth",
diff --git a/R/mlp_data.R b/R/mlp_data.R
index 81ad6dc83..73bf4df07 100644
--- a/R/mlp_data.R
+++ b/R/mlp_data.R
@@ -65,6 +65,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "mlp",
+  eng = "keras",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_fit(
   model = "mlp",
   eng = "keras",
@@ -77,6 +84,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "mlp",
+  eng = "keras",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "mlp",
   eng = "keras",
@@ -191,6 +205,7 @@ set_model_arg(
   func = list(pkg = "dials", fun = "penalty"),
   has_submodel = FALSE
 )
+
 set_model_arg(
   model = "mlp",
   eng = "nnet",
@@ -199,6 +214,7 @@ set_model_arg(
   func = list(pkg = "dials", fun = "epochs"),
   has_submodel = FALSE
 )
+
 set_fit(
   model = "mlp",
   eng = "nnet",
@@ -211,6 +227,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "mlp",
+  eng = "nnet",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_fit(
   model = "mlp",
   eng = "nnet",
@@ -223,6 +246,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "mlp",
+  eng = "nnet",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "mlp",
   eng = "nnet",
diff --git a/R/multinom_reg_data.R b/R/multinom_reg_data.R
index 5ef051c9c..0eb392c53 100644
--- a/R/multinom_reg_data.R
+++ b/R/multinom_reg_data.R
@@ -37,6 +37,12 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "multinom_reg",
+  eng = "glmnet",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
 
 set_pred(
   model = "multinom_reg",
@@ -129,6 +135,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "multinom_reg",
+  eng = "spark",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "multinom_reg",
   eng = "spark",
@@ -192,6 +205,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "multinom_reg",
+  eng = "keras",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "multinom_reg",
   eng = "keras",
@@ -255,6 +275,12 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "multinom_reg",
+  eng = "nnet",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
 
 set_pred(
   model = "multinom_reg",
diff --git a/R/nearest_neighbor_data.R b/R/nearest_neighbor_data.R
index 61cad97c8..695c09cdc 100644
--- a/R/nearest_neighbor_data.R
+++ b/R/nearest_neighbor_data.R
@@ -47,6 +47,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "nearest_neighbor",
+  eng = "kknn",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_fit(
   model = "nearest_neighbor",
   eng = "kknn",
@@ -59,6 +66,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "nearest_neighbor",
+  eng = "kknn",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "nearest_neighbor",
   eng = "kknn",
diff --git a/R/nullmodel_data.R b/R/nullmodel_data.R
index c8a29c41c..aa6345879 100644
--- a/R/nullmodel_data.R
+++ b/R/nullmodel_data.R
@@ -21,6 +21,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "null_model",
+  eng = "parsnip",
+  mode = "regression",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_fit(
   model = "null_model",
   eng = "parsnip",
@@ -33,6 +40,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "null_model",
+  eng = "parsnip",
+  mode = "classification",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_pred(
   model = "null_model",
   eng = "parsnip",
diff --git a/R/rand_forest_data.R b/R/rand_forest_data.R
index 70b872ae4..a4c81b669 100644
--- a/R/rand_forest_data.R
+++ b/R/rand_forest_data.R
@@ -132,6 +132,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "rand_forest",
+  eng = "ranger",
+  mode = "classification",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_fit(
   model = "rand_forest",
   eng = "ranger",
@@ -149,6 +156,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "rand_forest",
+  eng = "ranger",
+  mode = "regression",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_pred(
   model = "rand_forest",
   eng = "ranger",
@@ -338,6 +352,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "rand_forest",
+  eng = "randomForest",
+  mode = "classification",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_fit(
   model = "rand_forest",
   eng = "randomForest",
@@ -351,6 +372,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "rand_forest",
+  eng = "randomForest",
+  mode = "regression",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_pred(
   model = "rand_forest",
   eng = "randomForest",
@@ -475,6 +503,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "rand_forest",
+  eng = "spark",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_fit(
   model = "rand_forest",
   eng = "spark",
@@ -488,6 +523,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "rand_forest",
+  eng = "spark",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "rand_forest",
   eng = "spark",
diff --git a/R/surv_reg_data.R b/R/surv_reg_data.R
index 9cd243e33..f333766d2 100644
--- a/R/surv_reg_data.R
+++ b/R/surv_reg_data.R
@@ -29,6 +29,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "surv_reg",
+  eng = "flexsurv",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "surv_reg",
   eng = "flexsurv",
@@ -92,6 +99,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "surv_reg",
+  eng = "survival",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "surv_reg",
   eng = "survival",
diff --git a/R/svm_poly_data.R b/R/svm_poly_data.R
index cf6b85ed4..e0eb3a867 100644
--- a/R/svm_poly_data.R
+++ b/R/svm_poly_data.R
@@ -57,6 +57,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "svm_poly",
+  eng = "kernlab",
+  mode = "regression",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_fit(
   model = "svm_poly",
   eng = "kernlab",
@@ -70,6 +77,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "svm_poly",
+  eng = "kernlab",
+  mode = "classification",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_pred(
   model = "svm_poly",
   eng = "kernlab",
diff --git a/R/svm_rbf_data.R b/R/svm_rbf_data.R
index c6eb11a9d..a210d5703 100644
--- a/R/svm_rbf_data.R
+++ b/R/svm_rbf_data.R
@@ -49,6 +49,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "svm_rbf",
+  eng = "kernlab",
+  mode = "regression",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_fit(
   model = "svm_rbf",
   eng = "kernlab",
@@ -62,6 +69,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "svm_rbf",
+  eng = "kernlab",
+  mode = "classification",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_pred(
   model = "svm_rbf",
   eng = "kernlab",
@@ -178,6 +192,14 @@ set_fit(
     )
   )
 )
+
+set_encoding(
+  model = "svm_rbf",
+  eng = "liquidSVM",
+  mode = "regression",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_fit(
   model = "svm_rbf",
   eng = "liquidSVM",
@@ -192,6 +214,14 @@ set_fit(
     )
   )
 )
+
+set_encoding(
+  model = "svm_rbf",
+  eng = "liquidSVM",
+  mode = "classification",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_pred(
   model = "svm_rbf",
   eng = "liquidSVM",
diff --git a/man/set_new_model.Rd b/man/set_new_model.Rd
index 385c3f83b..8d9642cf0 100644
--- a/man/set_new_model.Rd
+++ b/man/set_new_model.Rd
@@ -13,6 +13,8 @@
 \alias{get_pred_type}
 \alias{show_model_info}
 \alias{pred_value_template}
+\alias{set_encoding}
+\alias{get_encoding}
 \title{Tools to Register Models}
 \usage{
 set_new_model(model)
@@ -38,6 +40,10 @@ get_pred_type(model, type)
 show_model_info(model)
 
 pred_value_template(pre = NULL, post = NULL, func, ...)
+
+set_encoding(model, mode, eng, options)
+
+get_encoding(model)
 }
 \arguments{
 \item{model}{A single character string for the model type (e.g.
@@ -79,6 +85,12 @@ results.}
 \item{...}{Optional arguments that should be passed into the \code{args} slot for
 prediction objects.}
 
+\item{options}{A list of options for engine-specific encodings. Currently,
+the option implemented is \code{predictor_indicators} which tells \code{parsnip}
+whether the pre-processing should make indicator/dummy variables from factor
+predictors. This only affects cases when \code{\link[=fit.model_spec]{fit.model_spec()}} is used and the
+underlying model has an x/y interface.}
+
 \item{arg}{A single character string for the model argument name.}
 
 \item{fit_obj}{A list with elements \code{interface}, \code{protect},
diff --git a/tests/testthat/test_convert_data.R b/tests/testthat/test_convert_data.R
index 51cb4e221..c58364be2 100644
--- a/tests/testthat/test_convert_data.R
+++ b/tests/testthat/test_convert_data.R
@@ -168,6 +168,21 @@ test_that("numeric y and mixed x", {
   )
 })
 
+test_that("mixed x, no dummies, compare to a model that does not create dummies", {
+  expected <- rpart::rpart(rate ~ ., data = Puromycin)
+  data_classes <- attr(expected$terms, "dataClasses")[2:3]
+  observed <- parsnip:::convert_form_to_xy_fit(rate ~ ., data = Puromycin, indicators = FALSE)
+  expect_equal(names(data_classes), names(observed$x))
+  expect_equal(unname(data_classes), c("numeric", "factor"))
+  expect_s3_class(observed$x$state, "factor")
+  expect_equivalent(Puromycin$rate, observed$y)
+  expect_equal(expected$terms, observed$terms)
+
+  expect_null(observed$weights)
+  expect_null(observed$offset)
+})
+
+
 test_that("numeric y and mixed x, omit missing data", {
   expected <- lm(rate ~ ., data = Puromycin_miss, x = TRUE, y = TRUE)
   observed <- parsnip:::convert_form_to_xy_fit(rate ~ ., data = Puromycin_miss)