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# Tools

## Landscaping and Parameter Tuning

`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).

> - svm-landscape: landscaping and regularization parameter (C) tuning
> - fda-landscape: landscaping and regularization parameter (C) tuning
> - srda-landscape: landscaping and alpha parameter (alpha) tuning
> - pda-landscape: landscaping and number of regressions parameter (Nreg) tuning
> - dlda-landscape
> - nn-landscape: landscaping

Error (`mlpy.err()`), Matthews Correlation Coefficient (`mlpy.mcc()`) and optionally Canberra Distance (`mlpy.canberra()`) are retrieved at each parameter step.

`mlpy` includes executable scripts to be used exclusively for parameter tuning tasks:

> - irelief-sigma: kernel width parameter (sigma) tuning

In order to print help message:

    $ command --help

## Other Tools

borda

Compute Borda Count, Extraction Indicator, Mean Position Indicator from a text file containing feature lists.

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:

    $ 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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