Date: Tue, 26 Sep 2023 21:12:27 +0200 (CEST) Message-ID: <1168904068.1199.1695755547964@odysseus.offis.uni-oldenburg.de> Subject: Exported From Confluence MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_Part_1198_1848402577.1695755547964" ------=_Part_1198_1848402577.1695755547964 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Content-Location: file:///C:/exported.html Machine Learning

# Machine Learning

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

# Ensembles

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 (speedandpowe= r) and another one containing  the windspeed (e.g. a forecast).

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

One example:

=20
```/=
// create the first classifier by using SMO
smo =3D CLASSIFICATION_LEARN({
class=3D'power',
learner =3D 'weka',
algorithm =3D ['model'=3D'SMO-REGRESSION']
},
speedandpower
)
/// create the second classifier by using gaussian processes
gaussian =3D CLASSIFICATION_LEARN({
class=3D'power',
learner =3D 'weka',
algorithm =3D ['model'=3D'GAUSSIAN-PROCESSES']
},
speedandpower
)
/// create the thirs classifier by using a linear regression
linear =3D CLASSIFICATION_LEARN({
class=3D'power',
learner =3D 'weka',
algorithm =3D ['model'=3D'LINEAR-REGRESSION']
},
speedandpower
)  =20
/// union them all into one stream
unioned =3D UNION(smo, gaussian, linear)

/// then, classify them - each tuple will be classified by using all three =
classifiers
ensemble =3D CLASSIFY(speed, unioned)

/// aggregate the clazz using average - which allows a weighted kind of vot=
ing
agg =3D AGGREGATE({
aggregations=3D[           =20
['AVG', 'clazz', 'powerForecast', 'DOUBLE']
]
},
ensemble
)

```
=20