Usage Model: Training and Prediction

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 Algorithms Training Stage Workflow

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 n x p numeric table with the training data set. This table can be an object of any class derived from NumericTable.

labels

Pointer to the n x 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.

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 Algorithms Prediction Stage Workflow

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 n x 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.

Result ID

Result

prediction

Pointer to the n x 1 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.

Accessing API References

Intel® DAAL provides application programming interfaces for C++, Java*, and Python* languages. Visit Intel® Data Analytics Acceleration Library API Reference to download API References for C++, Java*, and Python*. API Reference for C++ is also available online on IDZ, see C++ API Reference for Intel® Data Analytics Acceleration Library.

For more complete information about compiler optimizations, see our Optimization Notice.
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