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Download
Paramsearch is free software; you can redistribute it and/or modify it
under the terms of
the GNU General Public
License (version 3) as published by
the Free Software Foundation
Documents
What does it do
On the basis of a data file containing a list of examples of some
classification task, where each example is represented by a list of
feature values and a class label, paramsearch searches for a
combination of algorithmic parameters of a machine learning algorithm
that it estimates to do well on unseen material from the same
source as the input instance base. Paramsearch implements two
heuristics for search in multi-dimensional algorithmic parameter
spaces:
- cross-validated classifier wrapping, recombining parameter settings pseudo-exhaustively, for small data sets (less than 1000 instances);
- wrapped progressive sampling for larger data sets (>=1000 instances).
Contact
Comments and questions are welcome; please direct them to Antal.vdnBosch (at) uvt.nl.
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Paramsearch works with
| TiMBL |
TiMBL algorithms: IB1, IGTree, TRIBL2 |
| Fambl |
Family-based learning |
| Ripper |
Rule learning, by William Cohen |
| C4.5 |
Decision tree induction, by J. Ross Quinlan |
| SNoW |
Sparse Networks of Winnows, by Dan Roth and colleagues. Also works with the perceptron implemented in SNoW |
| Maxent |
Maximum entropy toolkit, by Zhang Le |
| SVM-Light |
Support vector machines, by Thorsten Joachims |
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Paramsearch draws multiple learning curves in a
metaphorical mountaineering competition. Each curve represents the
generalization performance on heldout data of one combination of
parameter settings at increasing amounts of training data. In the end,
one setting wins, as other lower competing setting combinations are
removed from the competition at regular intervals.
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