Meta-data
Donated on 2/29/1996
Meta-Data was used in order to give advice about which classification method is appropriate for a particular dataset (taken from results of Statlog project).
Dataset Characteristics
Multivariate
Subject Area
Other
Associated Tasks
Classification
Feature Type
Categorical, Integer, Real
# Instances
528
# Features
-
Dataset Information
Additional Information
This DataSet is about the results of Statlog project. The project performed a comparative study between Statistical, Neural and Symbolic learning algorithms. Project StatLog (Esprit Project 5170) was concerned with comparative studies of different machine learning, neural and statistical classification algorithms. About 20 different algorithms were evaluated on more than 20 different datasets. The tests carried out under project produced many interesting results. The results of these tests are comprehensively described in a book (D.Michie et.al, 1994).
Has Missing Values?
Yes
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no |
0 to 10 of 22
Additional Variable Information
1. DS_Name categorical Name of DataSet 2. T continuous Number of examples in test set 3. N continuous Number of examples 4. p continuous Number of attributes 5. k continuous Number of classes 6. Bin continuous Number of binary Attributes 7. Cost continuous Cost (1=yes,0=no) 8. SDratio continuous Standard deviation ratio 9. correl continuous Mean correlation between attributes 10. cancor1 continuous First canonical correlation 11. cancor2 continuous Second canonical correlation 12. fract1 continuous First eigenvalue 13. fract2 continuous Second eigenvalue 14. skewness continuous Mean of |E(X-Mean)|^3/STD^3 15. kurtosis continuous Mean of |E(X-Mean)|^4/STD^4 16. Hc continuous Mean entropy of attributes 17. Hx continuous Entropy of classes 18. MCx continuous Mean mutual entropy of class and attributes 19. EnAtr continuous Equivalent number of attributes 20. NSRatio continuous Noise-signal ratio 21. Alg_Name categorical Name of Algorithm 22. Norm_error continuous Normalized Error (continuous class)
Dataset Files
| File | Size |
|---|---|
| meta.data | 66.9 KB |
| meta.names | 4.7 KB |
| Index | 110 Bytes |
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset meta_data = fetch_ucirepo(id=65) # data (as pandas dataframes) X = meta_data.data.features y = meta_data.data.targets # metadata print(meta_data.metadata) # variable information print(meta_data.variables)
Meta-data [Dataset]. (1994). UCI Machine Learning Repository. https://doi.org/10.24432/C5X31P.
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.