add_linear_regression_constr

gurobi_ml.sklearn.linear_regression.add_linear_regression_constr(gp_model, linear_regression, input_vars, output_vars=None, **kwargs)

Formulate linear_regression in gp_model.

The formulation predicts the values of output_vars using input_vars according to linear_regression. See our Users Guide for details on the mip formulation used.

Parameters:

• gp_model (gurobipy model) – The gurobipy model where the predictor should be inserted.

• linear_regression (sklearn.linear_model.LinearRegression) –

  The linear regression to insert. It can be of any of the following types:

  • sklearn.linear_model.LinearRegression

  • sklearn.linear_model.Ridge

  • sklearn.linear_model.Lasso

  input_vars: mvar_array_like

  Decision variables used as input for random forest in model.

  output_vars: mvar_array_like, optional

  Decision variables used as output for random forest in model.

Returns:

Object containing information about what was added to gp_model to formulate linear_regression.

Return type:

LinearRegressionConstr

Notes

See add_predictor_constr for acceptable values for input_vars and output_vars