Machine Learning Software Package

A Machine Learning Software Package is a software package that can be used to solve an ML Task.

• Context:
  • It can be used to implement an ML-based System (that solves a Machine Learning Task).
  • It can (often) provide a Machine Learning Library.
  • It can (sometimes) enable the implementation of an ML Algorithm.
• Example(s):
  • a Decision Tree Learning Tool, such as C4.5, and caret R package.
  • a CRF Learning Tool, such as: MALLET and CRF++.
  • CN2.
  • SVMlight.
  • Weka.
  • SAS Software.
  • SPSS Software.
  • scikit-learn.
  • …
• Counter-Example(s):
  • a Statistical Analysis System, because they focus on quantitative analysis.
  • a Data Mining System, because they focus on knowledge discovery.
  • a Rules-based System.
  • a Machine Learning Library, Machine Learning Package.
• See: Induction System, Predictive Modeling System.

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References

2000

• (Witten & Frank, 2000) ⇒ Ian H. Witten, and Eibe Frank. (2000). “Data Mining: Practical Machine Learning Tools and Techniques with Java implementations." Morgan Kaufmann.
  • Machine learning systems can use a wide variety of other information about attributes. For instance, dimensional considerations could be used to restrict the search to expressions or comparisons that are dimensionally correct. Circular ordering could affect the kinds of tests that are considered. For example, in a temporal context, tests on a "day" attribute could involve next day, previous day, next week, same day next week. Partial orderings, that, generalize/specialization relations, frequently occur in practical situations. Information this kind is often referred to as metadata, data about data. However, the kind of practical schemes currently used for data mining are rarely capable of taking metadata into account, although it is likely that these capabilities will develop rapidly in the future.

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