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Ishwar

Ishwar

active ARFF Public Domain (CC0) Visibility: public Uploaded 08-07-2020 by Ishwar Nagwani
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  • Machine Learning Transportation
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hydraulic

22 features

ps1numeric2204 unique values
0 missing
ps2numeric2205 unique values
0 missing
ps3numeric2203 unique values
0 missing
ps4numeric966 unique values
0 missing
ps5numeric2204 unique values
0 missing
ps6numeric2204 unique values
0 missing
ts1numeric2202 unique values
0 missing
ts2numeric2200 unique values
0 missing
ts3numeric2199 unique values
0 missing
ts4numeric2198 unique values
0 missing
fs1numeric2195 unique values
0 missing
fs2numeric2200 unique values
0 missing
eps1numeric2205 unique values
0 missing
vs1numeric2087 unique values
0 missing
senumeric2204 unique values
0 missing
cenumeric2198 unique values
0 missing
cpnumeric2128 unique values
0 missing
cooler_conditionnominal3 unique values
0 missing
valve_conditionnominal4 unique values
0 missing
internal_pump_leakagenominal3 unique values
0 missing
hydraulic_accumulatornominal4 unique values
0 missing
stablenominal2 unique values
0 missing

19 properties

2205
Number of instances (rows) of the dataset.
22
Number of attributes (columns) of the dataset.
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.
17
Number of numeric attributes.
5
Number of nominal attributes.
0.01
Number of attributes divided by the number of instances.
77.27
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
22.73
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.
1
Number of binary attributes.
4.55
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

7 tasks

0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: precision - target_feature: cooler_condition - cost matrix: adam
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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