evaluate: Evaluate a Data Stream Mining Task

In stream: Infrastructure for Data Stream Mining

Evaluate a Data Stream Mining Task

Description

Generic function to calculate evaluation measures for a data stream mining task DST on a data stream DSD object.

Usage

evaluate_static(object, dsd, measure, n, ...)

evaluate_stream(object, dsd, measure, n, horizon, ..., verbose = FALSE)

Arguments

object	The DST object that the evaluation measure is being requested from.
dsd	The DSD object used to create the test data.
measure	Evaluation measure(s) to use. If missing then all available measures are returned.
n	The number of data points being requested.
...	Further arguments are passed on to the specific implementation (e.g., see evaluate.DSC)
horizon	Evaluation is done using horizon many previous points (see detail section).
verbose	Report progress?

Details

We define two generic evaluation functions:

• evaluate_static() evaluates the current DST model on new data without updating the model.

• evaluate_stream() evaluates the DST model using prequential error estimation (see Gama, Sebastiao and Rodrigues; 2013). The data points in the horizon are first used to calculate the evaluation measure and then they are used for updating the cluster model. A horizon of ' means that each point is evaluated and then used to update the model.

The available evaluation measures depend on the task. Currently available task to evaluate:

• DSC via evaluate.DSC

Value

evaluate returns an object of class stream_eval which is a numeric vector of the values of the requested measures.

Author(s)

Michael Hahsler

References

Joao Gama, Raquel Sebastiao, Pedro Pereira Rodrigues (2013). On evaluating stream learning algorithms. Machine Learning, March 2013, Volume 90, Issue 3, pp 317-346.

See Also

Other DST: DSAggregate(), DSC(), DSClassifier(), DSOutlier(), DSRegressor(), DST(), DST_SlidingWindow(), DST_WriteStream(), predict(), stream_pipeline, update()

Other evaluation: animate_cluster(), evaluate.DSC

stream documentation built on May 29, 2024, 9:43 a.m.