SDSCATTER Interactive scatter plot and visualization of classifier outputs

 Interactive scatter plot
   SH=SDSCATTER(DATA)

 Visualization of classifier outputs
   SH=SDSCATTER(DATA,P)
   SH=SDSCATTER(DATA,PD,'roc');     % open scatter and ROC plot
   SH=SDSCATTER(DATA,PD,'roc',FIG); % open scatter and use ROC plot in FIG

 INPUT
   DATA     data set
   P        pipeline object
   PD       pipeline object returning decisions
   FIG      Figure handle of ROC plot opened with SDDRAWROC

 OUTPUT
   SH       handle of the scatter figure

 OPTIONS
 'callback' Function handle of a callback (see perclass_example_callback.m)

 DESCRIPTION
 SDSCATTER provides interactive scatter plot and visualization of
 pipeline outputs. See Scatter menu for interactive functionality.
 Interactive keyboard commands:
  cursor keys : change features on x (left/right) and y (up/down) axis
  =/- : change z-order of classes (for highly overlapping data)
   : cycle through classes one at a time
  l   : legend on/off
  h   : hide this class
  o   : show only this class
  t   : this class to top
  1:9 : switch to property (see Scatter/Use property menu)
  a   : switch between full data set and subset axes
  d   : show feature distributions
  f   : return to previous sample filter
  F   : reset sample filter (show all samples)

 SDSCATTER can visualize decisions of classifier P in multi-D spaces or
 soft outputs in 2D. If PD stores ROC, SDSCATTER with 'roc' option shows
 also ROC plot and allows inspection of classifier decisions at different
 operating points.

 READ MORE
 http://perclass.com/doc/guide/visualization.html#sdscatter

sdscatter is referenced in examples:

• kb22: Note on decision tree performance and speed
• kb21: Feature selection in perClass
• kb19: PRTools compatibility
• kb18: How to protect a trained discriminant against outliers?
• kb17: How to optimize three-class classifier in imbalanced problems
• kb16: Visualize the effect of a change of parameters in a trained classifier
• kb15: How to speed up classifiers using the neural network approximation?
• kb12: Detector classifier cascade with ROC analysis
• kb11: Hierarchical classifier: How to build detector-classifier cascade?
• kb10: A step by step construction of a detector
• kb8: How to build a detector in a single line of code?
• kb7: How to convert LIBSVM Support Vector machine into a pipeline?