details_decision_tree_rpart | R Documentation |
rpart::rpart()
fits a model as a set of if/then
statements that
creates a tree-based structure.
For this engine, there are multiple modes: classification, regression, and censored regression
This model has 3 tuning parameters:
tree_depth
: Tree Depth (type: integer, default: 30L)
min_n
: Minimal Node Size (type: integer, default: 2L)
cost_complexity
: Cost-Complexity Parameter (type: double, default:
0.01)
decision_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) %>% set_engine("rpart") %>% set_mode("classification") %>% translate()
## Decision Tree Model Specification (classification) ## ## Main Arguments: ## cost_complexity = double(1) ## tree_depth = integer(1) ## min_n = integer(1) ## ## Computational engine: rpart ## ## Model fit template: ## rpart::rpart(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), ## cp = double(1), maxdepth = integer(1), minsplit = min_rows(0L, ## data))
decision_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) %>% set_engine("rpart") %>% set_mode("regression") %>% translate()
## Decision Tree Model Specification (regression) ## ## Main Arguments: ## cost_complexity = double(1) ## tree_depth = integer(1) ## min_n = integer(1) ## ## Computational engine: rpart ## ## Model fit template: ## rpart::rpart(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), ## cp = double(1), maxdepth = integer(1), minsplit = min_rows(0L, ## data))
The censored extension package is required to fit this model.
library(censored) decision_tree( tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1) ) %>% set_engine("rpart") %>% set_mode("censored regression") %>% translate()
## Decision Tree Model Specification (censored regression) ## ## Main Arguments: ## cost_complexity = double(1) ## tree_depth = integer(1) ## min_n = integer(1) ## ## Computational engine: rpart ## ## Model fit template: ## pec::pecRpart(formula = missing_arg(), data = missing_arg(), ## weights = missing_arg(), cp = double(1), maxdepth = integer(1), ## minsplit = min_rows(0L, data))
This engine does not require any special encoding of the predictors.
Categorical predictors can be partitioned into groups of factor levels
(e.g. {a, c}
vs {b, d}
) when splitting at a node. Dummy variables
are not required for this model.
This model can utilize case weights during model fitting. To use them,
see the documentation in case_weights and the examples
on tidymodels.org
.
The fit()
and fit_xy()
arguments have arguments called
case_weights
that expect vectors of case weights.
Predictions of type "time"
are predictions of the mean survival time.
This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.
The “Fitting and Predicting with parsnip” article contains
examples
for decision_tree()
with the "rpart"
engine.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.
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