Visible to Intel only — GUID: GUID-6A7AA151-00E5-4047-AAB1-6C126965D61B
Visible to Intel only — GUID: GUID-6A7AA151-00E5-4047-AAB1-6C126965D61B
Logistic Loss
LogisticLoss is a common objective function used for binary classification.
Operation |
Computational methods |
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Mathematical formulation
Computing
Algorithm takes dataset with n feature vectors of dimension p, vector with correct class labels and coefficients vector of size as input. Then it calculates logistic loss, its gradient or gradient using the following formulas.
Value
, where - predicted probabilities, - sigmoid function. Note that probabilities are binded to interval to avoid problems with computing log function ( if float type is used and otherwise)
Gradient
, where , for
Hessian
, where , , for
Computation method: dense_batch
The method computes value of objective function, its gradient or hessian for the dense data. This is the default and the only method supported.
Programming Interface
Refer to API Reference: LogisticLoss.
Distributed mode
Currently algorithm does not support distributed execution in SMPD mode.