HDF  n`  TREEHEAPX datadata_descr8 (name'servo' (mldata0 H comment' 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  0TREEHEAPX(Pscrewdouble1int0motor0SNOD 0Hp LSNOD-. /~TUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSGCOLAEEADCAE A A E D DBDBCEDBCDBBBADADAA A!C"D#A$D%B&A'A(E)A*A+C,A-B.C/D0C1E2B3D4B5B6C7E8B9E:E;E<B=C>C?A@DACBACADEEEFBGDHDIBJBKBLAMANEOCPEQDRBSATCUDVAWBXAYDZE[D\A]B^E_D`BaEbAcDdAeCfBgAhBiAjAkBlBmCnAoDpAqArBsEtCuEvCwBxDyDzA{C|E}A~DCCBCAEDBECEACEECBDBBCCCCBCCBEDAAAAEABCDDEEBE ?@4 458L :8L(@p L103TREEZ HEAPX(P3namesorderingtypes04PLJJJJJ\\J\\\\\\-1?ad73??UD@??'I?}if@?'I@i??[{c?;3?-1??ad73? [f??[{c?Blf?ffffff?*pf?33333?[{c?u6䟙?u6䟙?;3?l`q83@[{c? [f?-3@?P}:3@Blf?Blf?ad73????Blf?ffffff?;3?=3?*pf?;3?[{c???'I?i?S"^f@}ZEhf?i?N`@@ad73?6nf?L?=3??|г@"R.?u6䟙?u6䟙?i?Blf?6nf?ad73?nض(3??Blf?;3??'I?-1?Blf?ad73??ad73?-1?N`@@?`@[{c?N`@r߉ @"R.?N`@?'I?JJJJJJJJJJJJJJJJJJJJJJ{J|J}J~JJJJJJJJJJJJJJJJJJQJRJSJTJUJVJWJXJYJZJ[J\J]J^J_J`JaJbJcJdJeJfJgJhJiJjJkJlJmJnJoJpJqJrJsJtJuJvJwJxJyJzJJJJJJJJJ J J J J JJJJJJJJJJJJJJJJJJJ J!J"J#J$J%J&J'J(J)J*J+J,J-J.J/J0J1J2J3J4J5J6J7J8J9J:J;J<J=J>J?J@JAJBJCJDJEJFJGJHJIJJJKJLJMJNJOJPGCOLBBCACACA A E C B BBABDCCEDEDACCBEAAE E!C"B#C$C%C&C'C(C)D*A+C,E-D.E/D0A1B2A3A4B5C6E7B8A9A:A;E<B=D>A?E@BAEBDCEDAEAFEGAHDIAJDKCLBMBNCOAPAQCRBSBTAUBVEWCXDYDZC[B\C]B^A_A`BaEbBcEdEeEfCgDhCiDjDkClBmBnDoCpAqArAsEtBuAvEwExAyCzC{A|B}A~AEAACBBBCCCEDECCDCEBECEDBBCECACDBBDEDBEvgainclasspgainscrewmotorint0SNOD3[ l5@LGCOLdouble1screwmotornominal:4,5,3,2,1numericnominal:5,6,4,3nominal:E,D,A,B,Cnominal:E,B,D,C,AH5PL