1. Title: Servo Data

2. Sources
   (a) Created by: Karl Ulrich (MIT) in 1986
   (b) Donor: Ross Quinlan
   (c) Date: May 1993

3. Past Usage:

   1.  Quinlan, J.R., "Learning with continuous classes", Proc. 5th Australian
       Joint Conference on AI (eds A. Adams and L. Sterling), Singapore: World
       Scientific, 1992
 
   2.  Quinlan, J.R., "Combining instance-based and model-based learning",
       Proc. ML'93 (ed P.E. Utgoff), San Mateo: Morgan Kaufmann 1993
 
       Results on 10-way cross-validation:
 
       Method			Average		Relative
       ------			 |Err|		 Error
 				-------		--------
 
       Guessing mean		  1.15		  1.00
       Instance-based		   .52		   .26
       Regression		   .86		   .49
       Model trees		   .45		   .29
       Neural nets (G. Hinton)	   .30		   .11
       Regression+instances	   .48		   .20
       Model trees+instances	   .30		   .17
       NN+instances		   .29		   .11
 
4. Relevant Information:

   Ross Quinlan:

   This data was given to me by Karl Ulrich at MIT in 1986.  I didn't 
   record his description at the time, but here's his subsequent (1992) 
   recollection:
 
     "I seem to remember that the data was from a simulation of a servo
     system involving a servo amplifier, a motor, a lead screw/nut, and a
     sliding carriage of some sort.  It may have been on of the
     translational axes of a robot on the 9th floor of the AI lab.  In any
     case, the output value is almost certainly a rise time, or the time
     required for the system to respond to a step change in a position set
     point."
 
   (Quinlan, ML'93)

   "This is an interesting collection of data provided by Karl 
    Ulrich.  It covers an extremely non-linear phenomenon - predicting the 
    rise time of a servomechanism in terms of two (continuous) gain settings
    and two (discrete) choices of mechanical linkages."

5. Number of Instances: 167

6. Number of Attributes: 4 + numeric class attribute

7. Attribute information:

   1. motor: A,B,C,D,E
   2. screw: A,B,C,D,E
   3. pgain: 3,4,5,6
   4. vgain: 1,2,3,4,5
   5. class: 0.13 to 7.10

8. Missing Attribute Values: None