Example process

Create a representation of a simple machine learning modelling process, i.e.

• Open algorithm implementation (e.g. weka.J48)
• Load dataset (e.g. Iris.arff)
• Build model (e.g. decision tree)
• Store model (e.g. a .model file)

You can do this from the perspective of your favourite machine learning environment, but don't overspecialize. In enough detail so that anyone else can repeat the same thing and has all relevant information. Mention the core concepts that you need. Optionally, also state their relations (useful for later).

MEX Example

• iris-mex-output.ttl => mex file (metadata)
• iris-weka-output.txt => weka output
• iris.arff => dataset
• j48-iris.model => model file

ML Schema - RapidMiner Examples

ML-Schema-Example1 (SVM)

• Workflow
• Model

• ModelRepresentation

• Workflow representation in XML RapidMiner workflow in XML

Representation of ML-Schema-Example1 (SVM) using ML-Schema terms:

1. Level of Specification:

• //Samples/data/Golf->mls:Data
• //Local Repository/processes/ML-Schema-Example1->mls:Workflow
• Retrieve Golf -> mls:Implementation
• Select Attributes -> mls:Implementation
• SVM -> mls:Implementation
• Store ->mls:Implementation
• SVM.model->mls:Model

2. Level of execution:

• golfModelSerialization->mls:ModelRepresentation