Data
cpu

cpu

active ARFF Publicly available Visibility: public Uploaded 03-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Attributes 2 and 8 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: Relative CPU Performance Data 2. Source Information -- Creators: Phillip Ein-Dor and Jacob Feldmesser -- Ein-Dor: Faculty of Management; Tel Aviv University; Ramat-Aviv; Tel Aviv, 69978; Israel -- Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779 -- Date: October, 1987 3. Past Usage: 1. Ein-Dor and Feldmesser (CACM 4/87, pp 308-317) -- Results: -- linear regression prediction of relative cpu performance -- Recorded 34% average deviation from actual values 2. Kibler,D. & Aha,D. (1988). Instance-Based Prediction of Real-Valued Attributes. In Proceedings of the CSCSI (Canadian AI) Conference. -- Results: -- instance-based prediction of relative cpu performance -- similar results; no transformations required - Predicted attribute: cpu relative performance (numeric) 4. Relevant Information: -- The estimated relative performance values were estimated by the authors using a linear regression method. See their article (pp 308-313) for more details on how the relative performance values were set. 5. Number of Instances: 209 6. Number of Attributes: 10 (6 predictive attributes, 2 non-predictive, 1 goal field, and the linear regression's guess) 7. Attribute Information: 1. vendor name: 30 (adviser, amdahl,apollo, basf, bti, burroughs, c.r.d, cambex, cdc, dec, dg, formation, four-phase, gould, honeywell, hp, ibm, ipl, magnuson, microdata, nas, ncr, nixdorf, perkin-elmer, prime, siemens, sperry, sratus, wang) 2. Model Name: many unique symbols 3. MYCT: machine cycle time in nanoseconds (integer) 4. MMIN: minimum main memory in kilobytes (integer) 5. MMAX: maximum main memory in kilobytes (integer) 6. CACH: cache memory in kilobytes (integer) 7. CHMIN: minimum channels in units (integer) 8. CHMAX: maximum channels in units (integer) 9. PRP: published relative performance (integer) 10. ERP: estimated relative performance from the original article (integer) 8. Missing Attribute Values: None 9. Class Distribution: the class value (PRP) is continuously valued. PRP Value Range: Number of Instances in Range: 0-20 31 21-100 121 101-200 27 201-300 13 301-400 7 401-500 4 501-600 2 above 600 4 Summary Statistics: Min Max Mean SD PRP Correlation MCYT: 17 1500 203.8 260.3 -0.3071 MMIN: 64 32000 2868.0 3878.7 0.7949 MMAX: 64 64000 11796.1 11726.6 0.8630 CACH: 0 256 25.2 40.6 0.6626 CHMIN: 0 52 4.7 6.8 0.6089 CHMAX: 0 176 18.2 26.0 0.6052 PRP: 6 1150 105.6 160.8 1.0000 ERP: 15 1238 99.3 154.8 0.9665

8 features

class (target)numeric104 unique values
0 missing
vendornominal30 unique values
0 missing
MYCTnumeric60 unique values
0 missing
MMINnumeric25 unique values
0 missing
MMAXnumeric23 unique values
0 missing
CACHnumeric22 unique values
0 missing
CHMINnumeric15 unique values
0 missing
CHMAXnumeric31 unique values
0 missing

19 properties

209
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).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
7
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
-54.57
Average class difference between consecutive instances.
0
Percentage of missing values.
0.04
Number of attributes divided by the number of instances.
87.5
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
12.5
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.

19 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: Test on Training Data - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: root_mean_squared_error - target_feature: class
0 runs - estimation_procedure: Custom 10-fold Crossvalidation - 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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