Generic function to calculate evaluation measures for a data stream mining task DST on a data stream DSD object.
evaluate_static(object, dsd, measure, n, ...)evaluate_stream(object, dsd, measure, n, horizon, ..., verbose = FALSE)
evaluate returns an object of class stream_eval which
is a numeric vector of the values of the requested measures.
The DST object that the evaluation measure is being requested from.
The DSD object used to create the test data.
Evaluation measure(s) to use. If missing then all available measures are returned.
The number of data points being requested.
Further arguments are passed on to the specific implementation (e.g., see evaluate.DSC)
Evaluation is done using horizon many previous points (see detail section).
Report progress?
Michael Hahsler
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
Joao Gama, Raquel Sebastiao, Pedro Pereira Rodrigues (2013). On evaluating stream learning algorithms. Machine Learning, March 2013, Volume 90, Issue 3, pp 317-346.