col_gen_estimator.BooleanDecisionRuleClassifier

class col_gen_estimator.BooleanDecisionRuleClassifier(max_iterations=-1, C=10, p=1, rmp_solver_params='', rmp_solver='glop', master_ip_solver_params='', subproblem_solver='cbc', subproblem_params=[['']])

Binary classifier using boolean decision rule generation method.

Parameters:

max_iterationsint, default=-1

Maximum column generation iterations. Negative values removes the iteration limit and the problem is solved till optimality.

Cint,default=10,

A parameter used for controlling the overall complexity of decision rule.

pfloat,default=1

A parameter used for balancing the penalty between false negatives and false positives. Higher value of p would result in more penalty for the false negatives.

rmp_solver_params: string, default = “”,

Solver parameters for solving restricted master problem (rmp).

rmp_solver: string, default = “glop”,

Solver used for solving restricted master problem (rmp).

master_ip_solver_params: string, default = “”,

Solver parameters for solving the integer master problem.

subproblem_solver: string, default = “cbc”,

Solver used for solving subproblem.

subproblem_params: list of strings, default = [“”],

Parameters for solving the subproblem.

Attributes:

X_ndarray, shape (n_samples, n_features)

The input passed during fit().

y_ndarray, shape (n_samples,)

The labels passed during fit().

classes_ndarray, shape (n_classes,)

The classes seen at fit().

__init__(max_iterations=-1, C=10, p=1, rmp_solver_params='', rmp_solver='glop', master_ip_solver_params='', subproblem_solver='cbc', subproblem_params=[['']])

predict(X)

Predicts the class based on the solution of master problem. This method is abstract.

Parameters:

Xarray-like, shape (n_samples, n_features)

The input samples. The inputs should only contain values in {0,1}.

Returns:

yndarray, shape (n_samples,)

The label for each sample. The labels only contain values in {0,1}.