Data
scene

scene

active ARFF Publicly available Visibility: public Uploaded 14-03-2019 by Quay Au
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  • 2019_multioutput_paper Economics Machine Learning
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Multi-label dataset. The scene dataset is an image classification task where labels like Beach, Mountain, Field, Urban are assigned to each image.

300 features

Beach (target)nominal2 unique values
0 missing
Sunset (target)nominal2 unique values
0 missing
FallFoliage (target)nominal2 unique values
0 missing
Field (target)nominal2 unique values
0 missing
Mountain (target)nominal2 unique values
0 missing
Urban (target)nominal2 unique values
0 missing
Att1numeric2349 unique values
0 missing
Att257numeric2340 unique values
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Att2numeric2346 unique values
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Att258numeric2336 unique values
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Att3numeric2354 unique values
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Att259numeric2344 unique values
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Att4numeric2343 unique values
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Att260numeric2347 unique values
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Att5numeric2346 unique values
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Att261numeric2358 unique values
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Att6numeric2346 unique values
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Att7numeric2345 unique values
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Att263numeric2343 unique values
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Att9numeric2369 unique values
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Att267numeric2344 unique values
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Att70numeric2350 unique values
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Att72numeric2361 unique values
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Att73numeric2371 unique values
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Att82numeric2369 unique values
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Att83numeric2352 unique values
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Att84numeric2370 unique values
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Att85numeric2354 unique values
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Att86numeric2348 unique values
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Att87numeric2352 unique values
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Att88numeric2354 unique values
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Att89numeric2356 unique values
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Att90numeric2348 unique values
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Att91numeric2353 unique values
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Att92numeric2326 unique values
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Att93numeric2334 unique values
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Att94numeric2345 unique values
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Att95numeric2342 unique values
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Att96numeric2343 unique values
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Att97numeric2338 unique values
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Att98numeric2322 unique values
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Att99numeric2198 unique values
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Att100numeric2210 unique values
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Att101numeric2225 unique values
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Att102numeric2217 unique values
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Att103numeric2213 unique values
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Att104numeric2221 unique values
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Att106numeric2256 unique values
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Att107numeric2276 unique values
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Att108numeric2274 unique values
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Att110numeric2270 unique values
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Att190numeric2293 unique values
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Att194numeric2301 unique values
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Att195numeric2294 unique values
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Att198numeric2096 unique values
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Att212numeric2237 unique values
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Att214numeric2260 unique values
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Att215numeric2255 unique values
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Att217numeric2251 unique values
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Att218numeric2269 unique values
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Att219numeric2277 unique values
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Att220numeric2274 unique values
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Att221numeric2275 unique values
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Att222numeric2280 unique values
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Att223numeric2288 unique values
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Att224numeric2266 unique values
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Att245numeric2236 unique values
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Att246numeric2327 unique values
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Att248numeric2293 unique values
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Att249numeric2284 unique values
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Att250numeric2294 unique values
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Att251numeric2297 unique values
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Att252numeric2307 unique values
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Att253numeric2333 unique values
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Att254numeric2330 unique values
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Att255numeric2342 unique values
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Att256numeric2333 unique values
0 missing

62 properties

2407
Number of instances (rows) of the dataset.
300
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.
294
Number of numeric attributes.
6
Number of nominal attributes.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0.12
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
2.39
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
1.69
Mean skewness among attributes of the numeric type.
0.27
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
0.18
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.34
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-0.75
Minimum kurtosis among attributes of the numeric type.
2
Percentage of binary attributes.
0.19
Second quartile (Median) of standard deviation of attributes of the numeric type.
76.68
Maximum kurtosis among attributes of the numeric type.
0.03
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.72
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
6.96
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
98
Percentage of numeric attributes.
0.51
Third quartile of means among attributes of the numeric type.
2
The maximum number of distinct values among attributes of the nominal type.
-0.85
Minimum skewness among attributes of the numeric type.
2
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
7.31
Maximum skewness among attributes of the numeric type.
0.07
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
2.45
Third quartile of skewness among attributes of the numeric type.
0.26
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
0.26
First quartile of kurtosis among attributes of the numeric type.
0.21
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
0.13
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
8.19
Mean kurtosis among attributes of the numeric type.
6
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.31
Mean of means among attributes of the numeric type.
0.05
First quartile of skewness among attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.16
First quartile of standard deviation of attributes of the numeric type.

9 tasks

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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