Classification Usage Model

Intel® oneAPI Data Analytics Library Developer Guide and Reference

Download PDF

ID 772611

Date 7/13/2023

Version

Public

A newer version of this document is available. Customers should click here to go to the newest version.

Visible to Intel only — GUID: GUID-47522324-E651-4B2C-AB20-BC05E55A51E9

View Details

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

Classification Usage Model: 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.

Training Input for Classification Algorithms
Input ID	Input
`data`	Pointer to the $n i m e s p$ 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 $n i m e s 1$ 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.

Training Output for Classification 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

Classification Usage Model: 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.

Prediction Input for Classification Algorithms
Input ID	Input
`data`	Pointer to the $n i m e s p$ 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.

Prediction Output for Classification Algorithms
Result ID	Result
`prediction`	Pointer to the $n i m e s 1$ numeric table with classification results (class labels or confidence levels). NOTE: 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 $n \times nClasses$ , 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 $n \times nClasses$ , 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. NOTE: 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`.

Select Your Language

Using Intel.com Search

Quick Links

Recent Searches

Advanced Search

Only search in

Intel® oneAPI Data Analytics Library Developer Guide and Reference

Classification Usage Model

Algorithm-Specific Parameters

Training Stage

Prediction Stage