bayesianRegression (Stream Processor)

This function predicts using a Bayesian linear regression model.Bayesian linear regression allows determining the uncertainty of each prediction by estimating the full-predictive distribution

Syntax

streamingml:bayesianRegression(<STRING> model.name, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:bayesianRegression(<STRING> model.name, <INT> prediction.samples, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)

Query Parameters

Name	Description	Default Value	Possible Data Types	Optional	Dynamic
model.name	The name of the model to be used.		STRING	No	No
prediction.samples	The number of samples to be drawn to estimate the prediction.	1000	INT	Yes	No
model.feature	The features of the model that need to be attributes of the stream.		DOUBLE FLOAT INT LONG	No	Yes

Extra Return Attributes

Name	Description	Possible Types
prediction	The predicted value (double).	DOUBLE
confidence	Inverse of the standard deviation of the predictive distribution.	DOUBLE

Example 1

CREATE STREAM StreamA (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double);

from StreamA#streamingml:bayesianRegression('model1', attribute_0, attribute_1, attribute_2, attribute_3)
insert all events into OutputStream;

This query uses a Bayesian regression model named model1 to predict the label of the feature vector represented by attribute_0, attribute_1, attribute_2, and attribute_3. The predicted value is emitted to the OutputStream streamalong with the prediction confidence (std of predictive distribution) and the feature vector. As a result, the OutputStream stream is defined as follows: (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, prediction double, confidence double).