This function instantiates a HHDecisionTree model, it is used to induce classification or regression trees depending upon the value of the response parameter. It supports the parameters listed below. It is used to find the optimum value for min_node_impurity.
invoke_model(
X,
y,
try_this_min_imp,
iter,
response,
mni_n_folds,
mni_n_trees,
useIdentity,
seed
)The feature variables we will use to train the HHDecisionTree model on.
The target variable.
The minimum node impurity we will try on this invocation of the HHDecisionTree model.
The number of times function invoke_model has been invoked, used to increment a number that is passed to the function set.seed().
The response parameter is used to specify what type of model to build, either 'classify' for a classification tree model or 'regressor' for a regression tree model. The default is 'classify'.
The number of folds to pass to mni.control to be used to instantiate a HHDecisionTree model when searching for an optimum value of min_node_impurity.
The number of trees to pass to mni.control to be used to instantiate a HHDecisionTree model when searching for an optimum value of min_node_impurity.
The useIdentity parameter when set TRUE will result in hhcartr using the original training data to find the optimal splits rather than using the reflected data. The default value is FALSE.
The seed parameter is used to provide a seed value for the R random number generator to ensure repeatable experiments.
Returns statistics on the current run of the HHDecisionTree model.