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Tools
=====
Landscaping and Parameter Tuning
--------------------------------
:mod:`mlpy` includes executable scripts to be used off-the-shelf for landscaping and parameter
tuning tasks. The classification and optionally feature ranking operations are organized
in a sampling procedure (k-fold or Monte Carlo cross validation).
* :command:`svm-landscape`: landscaping and regularization parameter (*C*) tuning
* :command:`fda-landscape`: landscaping and regularization parameter (*C*) tuning
* :command:`srda-landscape`: landscaping and alpha parameter (*alpha*) tuning
* :command:`pda-landscape`: landscaping and number of regressions parameter (*Nreg*) tuning
* :command:`dlda-landscape`
* :command:`nn-landscape`: landscaping
Error (:func:`mlpy.err`), Matthews Correlation Coefficient (:func:`mlpy.mcc`) and
optionally Canberra Distance (:func:`mlpy.canberra`) are retrieved at each parameter
step.
:mod:`mlpy` includes executable scripts to be used exclusively for parameter tuning tasks:
* :command:`irelief-sigma`: kernel width parameter (*sigma*) tuning
In order to print help message:
.. code-block:: bash
$ command --help
Other Tools
-----------
:command:`borda`
Compute Borda Count, Extraction Indicator, Mean Position Indicator from a text file
containing feature lists.
:command:`canberra`
Compute mean Canberra distance indicator on top-k sublists from a text file
containing feature lists and one contanining the top-k positions.
In order to print help message:
.. code-block:: bash
$ command --help
The Feature Lists File
^^^^^^^^^^^^^^^^^^^^^^
The feature lists file is a plain text TAB-separated file where each row is a feature ranking
(a feature list).
Example::
feat6 [TAB] feat2 [TAB] ... [TAB] feat1
feat4 [TAB] feat1 [TAB] ... [TAB] feat7
feat4 [TAB] feat9 [TAB] ... [TAB] feat3
feat2 [TAB] feat3 [TAB] ... [TAB] feat9
feat8 [TAB] feat4 [TAB] ... [TAB] feat2
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