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<h2>Classification</h2>
The Classification panel allows to test, tweak and observe how
different algorithms perform classification on samples living in a
N-dimensional space: "the canvas".<br>
Classification can be Binary or Multi-Class, depending on whether
there are presently more than 2 classes of samples (different
colors) and whether the algorithm allows it.<br>
<br>
The canvas will display the results of the classification in
multiple layers, which can be changed using the display options.
These are:<br>
<ul>
<li>Samples: the original sample data, colors indicate class
labels</li>
<li>Learned Model: the classified labels obtained by the algorithm</li>
<li>Model Info: additional information from the algorithm
(gaussian position and shape, support vectors, etc.)</li>
<li>Density Map: (for 2D canvas only) classification result for
each coordinate in space</li>
</ul>
In the case of binary classification, the red color is used to
indicate the positive class (by default class #1) while white color
indicates the negative class. Varying degrees of blackness indicate
uncertainty (for algorithms that do not have harsh class
transitions)<br>
<br>
<span style="font-weight: bold;">In Practice</span><br>
The easiest way to perform classification is to:<br>
<ol>
<li>Draw some samples (left-click: class 1, right-click: class 0)</li>
<li>Click on "Classify"</li>
</ol>
This should train the algorithm and start painting the canvas with
the results of the classification.<br>
<br>
<span style="font-weight: bold;">Options and Commands</span><br>
The interface for classification (the right-hand side of the
Algorithm Options dialog) provides the following commands:<br>
<ul>
<li>Classify: perform the classification using the currently
selected algorithm and options</li>
<li>Clear: clear the current classifier model (does NOT clear the
data)</li>
<li>Show ROC: display the Reciever Operator Characteristic curve
for the current binary classification</li>
<li>Compare: adds the current algorithm and options to the Compare
dialog for batch comparisons</li>
</ul>
and the following options:<br>
<ul>
<li>Positive Class: (currently unused) defines the class to be
used as positive class (by default class #1)</li>
<li>Train / Test ratio: the ratio of samples in the canvas to be
used for training</li>
<li>Input Dimensions: determines the dimensions that should be
used for classification (unselected dimensions will be ignored)</li>
<li>Manual Selection: manually select the training samples
(overrides the Train/Test ratio option)<br>
</li>
</ul>
All other options are algorithm-dependent and should be described in
the help menu of the algorithm itself.<br>
<br>
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