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