Trains
Donated on 6/23/1994
2 data formats (structured, one-instance-per-line)
Dataset Characteristics
Multivariate
Subject Area
Other
Associated Tasks
Classification
Feature Type
Categorical
# Instances
10
# Features
-
Dataset Information
Additional Information
Notes: - Additional "background" knowledge is supplied that provides a partial ordering on some of the attribute values. - We are providing this dataset both in its original form and in a form similar to the more typical propositional datasets in our repository. Since the trains dataset records relations between attributes, this transformation was somewhat challenging. However, it may shed some insight on this problem for people who are more familiar with the simple one-instance-per-line dataset format. Hierarchy of values: if (cshape is one of {openrect,opentrap,ushaped,dblopnrect} then cshape is opentop if (cshape is one of {hexagon,ellipse,closedrect,jaggedtop,slopetop, engine} then cshape closedtop Prediction task: Determine concise decision rules distinguishing trains traveling east from those traveling west.
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no |
0 to 10 of 32
Additional Variable Information
The following format was used for the "transformed" dataset representation as found in trains.transformed.data (one instance per line): 1. Number_of_cars (integer in [3-5]) 2. Number_of_different_loads (integer in [1-4]) 3-22: 5 attributes for each of cars 2 through 5: (20 attributes total) - num_wheels (integer in [2-3]) - length (short or long) - shape (closedrect, dblopnrect, ellipse, engine, hexagon, jaggedtop, openrect, opentrap, slopetop, ushaped) - num_loads (integer in [0-3]) - load_shape (circlelod, hexagonlod, rectanglod, trianglod) 23-32: 10 Boolean attributes describing whether 2 types of loads are on adjacent cars of the train - Rectangle_next_to_rectangle (0 if false, 1 if true) - Rectangle_next_to_triangle (0 if false, 1 if true) - Rectangle_next_to_hexagon (0 if false, 1 if true) - Rectangle_next_to_circle (0 if false, 1 if true) - Triangle_next_to_triangle (0 if false, 1 if true) - Triangle_next_to_hexagon (0 if false, 1 if true) - Triangle_next_to_circle (0 if false, 1 if true) - Hexagon_next_to_hexagon (0 if false, 1 if true) - Hexagon_next_to_circle (0 if false, 1 if true) - Circle_next_to_circle (0 if false, 1 if true) 33. Class attribute (east or west) The number of cars vary between 3 and 5. Therefore, attributes referring to properties of cars that do not exist (such as the 5 attriubutes for the "5th" car when the train has fewer than 5 cars) are assigned a value of "-".
Dataset Files
| File | Size |
|---|---|
| trains.tar.Z | 16 KB |
| trains.supplement | 14.6 KB |
| trains.names | 6.7 KB |
| trains-original.data | 6.4 KB |
| east-west.info | 2.3 KB |
0 to 5 of 7
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset trains = fetch_ucirepo(id=103) # data (as pandas dataframes) X = trains.data.features y = trains.data.targets # metadata print(trains.metadata) # variable information print(trains.variables)
Michalski, R. & Stepp, R. (1977). Trains [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5M01V.
Creators
Ryszard Michalski
Robert Stepp
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.