This page describes how to use the machine learning (aka mining) bundle.

You can simply combine all operators with other operators in Odysseus to create ensembles.

If we have, for examaple a stream with windspeed and power (speedandpower) and another one containing the windspeed (e.g. a forecast).

Then, it is possible to create different regressionfunctions, use them and weight the regression results by an aggregation.

One example:

/// create the first classifier by using SMO smo = CLASSIFICATION_LEARN({ class='power', learner = 'weka', algorithm = ['model'='SMO-REGRESSION'] }, speedandpower ) /// create the second classifier by using gaussian processes gaussian = CLASSIFICATION_LEARN({ class='power', learner = 'weka', algorithm = ['model'='GAUSSIAN-PROCESSES'] }, speedandpower ) /// create the thirs classifier by using a linear regression linear = CLASSIFICATION_LEARN({ class='power', learner = 'weka', algorithm = ['model'='LINEAR-REGRESSION'] }, speedandpower ) /// union them all into one stream unioned = UNION(smo, gaussian, linear) /// then, classify them - each tuple will be classified by using all three classifiers ensemble = CLASSIFY(speed, unioned) /// aggregate the clazz using average - which allows a weighted kind of voting agg = AGGREGATE({ aggregations=[ ['AVG', 'clazz', 'powerForecast', 'DOUBLE'] ] }, ensemble ) |

to be added