Developer Guide

Contents

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
.
weights
Weights of the observations in the training data set. Argument is optional and is required by the selected algorithms.
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
.
probabilities
Numeric table of size
n
x
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
Numeric table of size
n
x
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.
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.

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*.
.

Product and Performance Information

1

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Notice revision #20110804