Mean Squared Error Algorithm
Mean Squared Error Algorithm is not supported on GPU.
Details
Given 
, a set of feature vectors 
,
and a set of respective responses 
,
the mean squared error (MSE) objective function 
is a function that has the format:
, a set of feature vectors ,
and a set of respective responses ,
the mean squared error (MSE) objective function is a function that has the format:
In oneDAL implementation of the MSE, the 
is represented as:
is represented as:
For a given set of the indices 
,
, 
, 
,
the value and the gradient of the sum of functions in the argument
,
, , ,
the value and the gradient of the sum of functions in the argument x
respectively have the format:
where
Computation
Algorithm Input
The mean squared error algorithm accepts the input described below.
Pass the
Input ID
as a parameter to the methods that provide input for your algorithm.
For more details, see Algorithms.Input ID | Input |
argument | A numeric table of size  with the input argument  of the objective function. |
data | A numeric table of size  with the data  . |
dependentVariables | A numeric table of size  with dependent variables . |
Optional Algorithm Input
The mean squared error algorithm accepts the optional input described below.
Pass the Optional
Input ID
as a parameter to the methods that provide input for your algorithm.
For more details, see Algorithms.Input ID | Input |
weights | Optional input.
Pointer to the  numeric table with weights of samples.
The input can be an object of any class derived from NumericTable
except for PackedTriangularMatrix and PackedSymmetricMatrix .By default, all weights are equal to 1 . |
gramMatrix | Optional input.
Pointer to the :mathL`p times p` numeric table with pre-computed Gram matrix.
The input can be an object of any class derived from NumericTable
except for PackedTriangularMatrix and PackedSymmetricMatrix .By default, the table is set to empty numeric table. |
Algorithm Parameters
The mean squared error algorithm has the following parameters.
Some of them are required only for specific values of the computation method parameter method:
Parameter | Default value | Description |
penaltyL1 | 0 | The numeric table of size  with L1 regularized coefficients. |
penaltyL2 | 0 | The numeric table of size with L2 regularized coefficients. |
interceptFlag | true | Flag to indicate whether or not to compute the intercept. |
algorithmFPType | float | The floating-point type that the algorithm uses for intermediate computations. Can be float or double . |
method | defaultDense | Performance-oriented computation method. |
numberOfTerms | Not applicable | The number of terms in the objective function. |
batchIndices | Not applicable | The numeric table of size  , where m is the batch size, with a batch of indices to be used to compute the function results.
If no indices are provided, the implementation uses all the terms in the computation.This parameter can be an object of any class derived from NumericTable
except for PackedTriangularMatrix and PackedSymmetricMatrix . |
resultsToCompute | gradient | The 64-bit integer flag that specifies which characteristics of the objective function to compute. Provide one of the following values to request a single characteristic or use bitwise OR to request a combination of the characteristics:
|
Algorithm Output
For the output of the mean squared error algorithm, see Output for objective functions.
Examples
C++ (CPU)
Java*
There is no support for Java on GPU.