Piecewise Polynomial Prediction
This topic describes the Piecewise Polynomial Prediction component.
Features
The Piecewise Polynomial Prediction component uses a model trained by the Piecewise Polynomial Regression component to make predictions.
This component supports only piecewise polynomial regression models.
Calculation logic principles
A piecewise polynomial is created by dividing the value range of an input variable into continuous intervals and fitting a polynomial to each interval. The advantage of a piecewise polynomial is that it can fit curves of any shape and localize the impact of random data points.
Parameters
IN port - Input parameters
Parameter Name | Description | Required | Input Data Type |
Model Application | Select the model type and a specific model. Then, configure the input data for the model. Only piecewise polynomial regression models are supported. | Yes | Feature variables: Integer or float Note If non-numeric data exists, an exception is thrown. |
OUT port - Output parameters
Parameter | Description | Required | Output Data Type |
OUT | For the output parameter configuration, see the target variable name in the output preview of the model application. | No | Float |
Output quality code description
The output quality code is determined as follows:
If the quality codes of all input variables are >= 192, the quality code of the output variable is 192.
If the quality code of any input variable is in the range [0, 192), the quality code of the output variable is 0.
If the quality codes of all input variables are -1, or if some are -1 and the rest are >= 192, the quality code of the output variable is -1.