Servo
Donated on 4/30/1993
Data was from a simulation of a servo system
Dataset Characteristics
Multivariate
Subject Area
Computer Science
Associated Tasks
Regression
Feature Type
Categorical, Integer
# Instances
167
# Features
-
Dataset Information
Additional 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."
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Demographic | Description | Units | Missing Values |
|---|---|---|---|---|---|---|
| no | ||||||
| no | ||||||
| no | ||||||
| no |
0 to 4 of 4
Additional Variable 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
Ulrich,Karl. (1993). Servo. UCI Machine Learning Repository. https://doi.org/10.24432/C5Q30F.
@misc{misc_servo_87,
author = {Ulrich,Karl},
title = {{Servo}},
year = {1993},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5Q30F}
}pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset servo = fetch_ucirepo(id=87) # data (as pandas dataframes) X = servo.data.features y = servo.data.targets # metadata print(servo.metadata) # variable information print(servo.variables)
Creators
Karl Ulrich
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.