Data
autoMpg

autoMpg

active ARFF Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Identifier attribute deleted. As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction using instance-based learning with encoding length selection. In Progress in Connectionist-Based Information Systems. Singapore: Springer-Verlag. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 1. Title: Auto-Mpg Data 2. Sources: (a) Origin: This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The dataset was used in the 1983 American Statistical Association Exposition. (c) Date: July 7, 1993 3. Past Usage: - See 2b (above) - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann. 4. Relevant Information: This dataset is a slightly modified version of the dataset provided in the StatLib library. In line with the use by Ross Quinlan (1993) in predicting the attribute "mpg", 8 of the original instances were removed because they had unknown values for the "mpg" attribute. The original dataset is available in the file "auto-mpg.data-original". "The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes." (Quinlan, 1993) 5. Number of Instances: 398 6. Number of Attributes: 9 including the class attribute 7. Attribute Information: 1. mpg: continuous 2. cylinders: multi-valued discrete 3. displacement: continuous 4. horsepower: continuous 5. weight: continuous 6. acceleration: continuous 7. model year: multi-valued discrete 8. origin: multi-valued discrete 9. car name: string (unique for each instance) 8. Missing Attribute Values: horsepower has 6 missing values

8 features

class (target)numeric129 unique values
0 missing
cylindersnominal5 unique values
0 missing
displacementnumeric82 unique values
0 missing
horsepowernumeric93 unique values
6 missing
weightnumeric351 unique values
0 missing
accelerationnumeric95 unique values
0 missing
modelnominal13 unique values
0 missing
originnominal3 unique values
0 missing

19 properties

398
Number of instances (rows) of the dataset.
8
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
6
Number of missing values in the dataset.
6
Number of instances with at least one value missing.
5
Number of numeric attributes.
3
Number of nominal attributes.
0
Percentage of binary attributes.
1.51
Percentage of instances having missing values.
-2.88
Average class difference between consecutive instances.
0.19
Percentage of missing values.
0.02
Number of attributes divided by the number of instances.
62.5
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
37.5
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

18 tasks

2 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: Custom 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: Test on Training Data - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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