Logistic Loss¶

LogisticLoss is a common objective function used for binary classification.

Operation	Computational methods
dense_batch	dense_batch

Mathematical formulation¶

All types and functions in this section are declared in the oneapi::dal::logloss_objective namespace.

template<typename Float = float, typename Method = method::by_default, typename Task = task::by_default> class descriptor¶

Template Parameters

Float – The floating-point type that the algorithm uses for intermediate computations. Can be float or double.
Method – Tag-type that specifies an implementation of algorithm. Can be method::dense_batch.
Task – Tag-type that specifies the type of the problem to solve. Can be task::compute.

Constructors

descriptor(double l1_regularization_coefficient = 0.0, double l2_regularization_coefficient = 0.0, bool fit_intercept = true)¶: Creates a new instance of the class with the given l1_regularization_coefficient, l2_regularization_coefficient and fit_intercept property values.

Properties

double l1_regularization_coefficient¶

The L1-regularization strength.

Getter & Setter: double get_l1_regularization_coefficient() const

auto & set_l1_regularization_coefficient(double value)
Invariants: l1_regularization_coefficient >= 0.0

double l2_regularization_coefficient¶

The L2-regularization strength.

Getter & Setter: double get_l2_regularization_coefficient() const

auto & set_l2_regularization_coefficient(double value)
Invariants: l2_regularization_coefficient >= 0.0

bool intercept_flag¶

The fit_intercept flag.

Getter & Setter: bool get_intercept_flag() const

auto & set_intercept_flag(bool fit_intercept)

Objective function

Pairwise Distances