| sample_rpart | R Documentation |
Sample the conditional distribution created by a CART model
sample_rpart(model, new_data, conf_data, ignore_zeros = TRUE)
model |
A "model_fit" object created by rpart |
new_data |
A data frame with predictors |
conf_data |
A data frame with original confidential predictors |
ignore_zeros |
Should a vector of all 0 observations return NA for
the l-diversity calculation. Defaults to |
A numeric vector of predictions
rpart_mod_reg <- parsnip::decision_tree() |>
parsnip::set_engine("rpart") |>
parsnip::set_mode(mode = "regression")
regression_rec <- recipes::recipe(inctot ~ ., data = acs_conf)
model_reg <- workflows::workflow() |>
workflows::add_model(spec = rpart_mod_reg) |>
workflows::add_recipe(recipe = regression_rec) |>
parsnip::fit(data = acs_conf)
set.seed(1)
sample1 <- sample_rpart(
model = model_reg,
new_data = acs_conf[1:3, ],
conf_data = acs_conf
)
rpart_mod_class <- parsnip::decision_tree() |>
parsnip::set_engine("rpart") |>
parsnip::set_mode(mode = "classification")
classification_rec <- recipes::recipe(hcovany ~ ., data = acs_conf)
model_reg <- workflows::workflow() |>
workflows::add_model(spec = rpart_mod_class) |>
workflows::add_recipe(recipe = classification_rec) |>
parsnip::fit(data = acs_conf)
set.seed(1)
sample1 <- sample_rpart(
model = model_reg,
new_data = acs_conf[1:10, ],
conf_data = acs_conf
)
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