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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 \(n \times p\) 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 \(n \times 1\) 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 \(n \times p\) 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 \(n \times 1\) 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 \(n \times \text{nClasses}\), 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 \(n \times \text{nClasses}\), 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.

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Usage Model: Training and Prediction

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Regression Usage Model