add_decision_tree_regressor_constr#
- gurobi_ml.sklearn.decision_tree_regressor.add_decision_tree_regressor_constr(gp_model, decision_tree_regressor, input_vars, output_vars=None, epsilon=0.0, **kwargs)#
Formulate decision_tree_regressor into gp_model.
The formulation predicts the values of output_vars using input_vars according to decision_tree_regressor. See our User’s Guide for details on the mip formulation used.
- Parameters:
gp_model (gurobipy model) – The gurobipy model where the predictor should be inserted.
decision_tree_regressor (
sklearn.tree.DecisionTreeRegressor) – The decision tree regressor to insert as predictor.input_vars (mvar_array_like) – Decision variables used as input for decision tree in model.
output_vars (mvar_array_like, optional) – Decision variables used as output for decision tree in model.
epsilon (float, optional) – Small value used to impose strict inequalities for splitting nodes in MIP formulations.
- Returns:
Object containing information about what was added to gp_model to formulate decision_tree_regressor
- Return type:
Notes
See
add_predictor_constrfor acceptable values for input_vars and output_varsWarning
Although decision trees with multiple outputs are tested they were never used in a non-trivial optimization model. It should be used with care at this point.