Developer Guide and Reference

  • 2021.3
  • 06/28/2021
  • Public Content
Contents

Classification Usage Model

A typical workflow for classification methods includes training and prediction, as explained below.

Algorithm-Specific Parameters

The parameters used by classification algorithms at each stage depend on a specific algorithm. For a list of these parameters, refer to the description of an appropriate classification algorithm.

Training Stage

At the training stage, classification algorithms accept 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
data
Pointer to the LaTex Math image. numeric table with the training data set. This table can be an object of any class derived from
NumericTable
.
weights
Weights of the observations in the training data set. Argument is optional, but it is required by the selected algorithms.
labels
Pointer to the LaTex Math image. numeric table with class labels.
This table can be an object of any class derived from NumericTable except
PackedSymmetricMatrix
and
PackedTriangularMatrix
.
At the training stage, classification algorithms calculate the result described below. Pass the
Result ID
as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.
Result ID
Result
model
Pointer to the classification model being trained. The result can only be an object of the
Model
class.

Prediction Stage

At the prediction stage, classification algorithms accept 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
data
Pointer to the LaTex Math image. numeric table with the working data set. This table can be an object of any class derived from
NumericTable
.
model
Pointer to the trained classification model. This input can only be an object of the
Model
class.
At the prediction stage, classification algorithms calculate the result described below. Pass the
Result ID
as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.
Result ID
Result
prediction
Pointer to the LaTex Math image. numeric table with classification results (class labels or confidence levels).
By default, this table is an object of the
HomogenNumericTable
class, but you can define it as an object of any class derived from
NumericTable
except
PackedSymmetricMatrix
and
PackedTriangularMatrix
.
probabilities
A numeric table of size LaTex Math image., containing probabilities of classes computed when the
computeClassProbabilities
option is enabled. This result table is available for selected algorithms, see corresponding algorithm documentation for details.
logProbabilities
A numeric table of size LaTex Math image., containing logarithms of classes’ probabilities computed when the
computeClassLogProbabilities
option is enabled. This result table is available for selected algorithms, see corresponding algorithm documentation for details.
By default, this table is an object of the
HomogenNumericTable
class, but you can define it as an object of any class derived from
NumericTable
except
PackedSymmetricMatrix
,
PackedTriangularMatrix
,
CSRNumericTable
.

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.