# Regression Usage Model¶

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

## Algorithm-Specific Parameters¶

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

## Training Stage¶

At the training stage, regression 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 Regression 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. Optional argument.

dependentVariables

Pointer to the $$n \times k$$ numeric table with responses ($$k$$ dependent variables). This table can be an object of any class derived from NumericTable except PackedSymmetricMatrix and PackedTriangularMatrix.

At the training stage, regression 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 Regression Algorithms

Result ID

Result

model

Pointer to the regression model being trained. The result can only be an object of the Model class.

## Prediction Stage¶

At the prediction stage, regression 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 Regression 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 regression model. This input can only be an object of the Model class.

At the prediction stage, regression 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 Regression Algorithms

Result ID

Result

prediction

Pointer to the $$n \times k$$ numeric table with responses ($$k$$ dependent variables).

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.