Visible to Intel only — GUID: GUID-36326501-4137-42A2-9B80-E652D30C35F2
Visible to Intel only — GUID: GUID-36326501-4137-42A2-9B80-E652D30C35F2
Objective function
Some classification algorithms are designed to minimize the selected objective function. On each iteration its’ gradient and sometimes hessian is calculated and model weights are updated using this information.
Operation |
Computational methods |
Programming Interface |
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Supported objective functions
Mathematical formulation
Computing
Algorithm takes dataset with n feature vectors of dimension p, vector with correct class labels and coefficients vector w = { w_0, ldots, w_p }`of size :math:`p + 1 as input. Then it calculates logistic loss, its gradient or hessian.
Value
- value of objective function.
Gradient
- gradient of objective function.
Hessian
- hessian of objective function.
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: Objective Function.
Distributed mode
Currently algorithm does not support distributed execution in SMPD mode.
Examples: Logistic Loss
oneAPI DPC++
Batch Processing:
oneAPI C++
Batch Processing: