OpenML
spellman_yeast

spellman_yeast

active ARFF Public Domain (CC0) Visibility: public Uploaded 04-06-2018 by Bilge Celik
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  • Computer Systems concept_drift Machine Learning
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Two colour spotted cDNA array data set of a series of experiments to identify which genes in Yeast are cell cycle regulated.

82 features

cln3-1numeric276 unique values
193 missing
cln3-2numeric275 unique values
365 missing
clbnumeric0 unique values
6178 missing
clb2-2numeric216 unique values
454 missing
clb2-1numeric234 unique values
142 missing
alphanumeric0 unique values
6178 missing
alpha0numeric332 unique values
165 missing
alpha7numeric327 unique values
525 missing
alpha14numeric276 unique values
191 missing
alpha21numeric264 unique values
312 missing
alpha28numeric214 unique values
267 missing
alpha35numeric204 unique values
207 missing
alpha42numeric213 unique values
123 missing
alpha49numeric203 unique values
257 missing
alpha56numeric216 unique values
147 missing
alpha63numeric207 unique values
186 missing
alpha70numeric230 unique values
185 missing
alpha77numeric213 unique values
178 missing
alpha84numeric188 unique values
155 missing
alpha91numeric178 unique values
329 missing
alpha98numeric209 unique values
209 missing
alpha105numeric175 unique values
174 missing
alpha112numeric207 unique values
222 missing
alpha119numeric209 unique values
251 missing
cdc15numeric0 unique values
6178 missing
cdc15_10numeric354 unique values
677 missing
cdc15_30numeric360 unique values
477 missing
cdc15_50numeric334 unique values
501 missing
cdc15_70numeric311 unique values
608 missing
cdc15_80numeric296 unique values
573 missing
cdc15_90numeric305 unique values
562 missing
cdc15_100numeric309 unique values
606 missing
cdc15_110numeric304 unique values
570 missing
cdc15_120numeric332 unique values
611 missing
cdc15_130numeric300 unique values
495 missing
cdc15_140numeric263 unique values
574 missing
cdc15_150numeric286 unique values
811 missing
cdc15_160numeric275 unique values
583 missing
cdc15_170numeric276 unique values
571 missing
cdc15_180numeric315 unique values
803 missing
cdc15_190numeric277 unique values
613 missing
cdc15_200numeric293 unique values
1014 missing
cdc15_210numeric268 unique values
573 missing
cdc15_220numeric294 unique values
741 missing
cdc15_230numeric316 unique values
596 missing
cdc15_240numeric316 unique values
847 missing
cdc15_250numeric336 unique values
379 missing
cdc15_270numeric269 unique values
537 missing
cdc15_290numeric317 unique values
426 missing
cdc28numeric0 unique values
6178 missing
cdc28_0numeric376 unique values
122 missing
cdc28_10numeric343 unique values
72 missing
cdc28_20numeric352 unique values
67 missing
cdc28_30numeric297 unique values
55 missing
cdc28_40numeric285 unique values
66 missing
cdc28_50numeric254 unique values
56 missing
cdc28_60numeric265 unique values
82 missing
cdc28_70numeric283 unique values
84 missing
cdc28_80numeric260 unique values
75 missing
cdc28_90numeric361 unique values
237 missing
cdc28_100numeric329 unique values
165 missing
cdc28_110numeric266 unique values
319 missing
cdc28_120numeric240 unique values
312 missing
cdc28_130numeric260 unique values
1439 missing
cdc28_140numeric248 unique values
2159 missing
cdc28_150numeric287 unique values
521 missing
cdc28_160numeric277 unique values
543 missing
elunumeric0 unique values
6178 missing
elu0numeric413 unique values
122 missing
elu30numeric271 unique values
153 missing
elu60numeric241 unique values
175 missing
elu90numeric230 unique values
132 missing
elu120numeric252 unique values
103 missing
elu150numeric236 unique values
119 missing
elu180numeric211 unique values
111 missing
elu210numeric181 unique values
118 missing
elu240numeric191 unique values
131 missing
elu270numeric198 unique values
110 missing
elu300numeric196 unique values
112 missing
elu330numeric217 unique values
112 missing
elu360numeric256 unique values
156 missing
elu390numeric180 unique values
114 missing

62 properties

6178
Number of instances (rows) of the dataset.
82
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
59017
Number of missing values in the dataset.
6178
Number of instances with at least one value missing.
82
Number of numeric attributes.
0
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.
0.01
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
3.78
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.
-0.04
Mean skewness among attributes of the numeric type.
0
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
0.38
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.
-0.08
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
0.27
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.38
Second quartile (Median) of standard deviation of attributes of the numeric type.
28.03
Maximum kurtosis among attributes of the numeric type.
-0.28
Minimum of means among attributes of the numeric type.
100
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.18
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
11.65
Percentage of missing values.
7.03
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
100
Percentage of numeric attributes.
0.03
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-2.05
Minimum skewness among attributes of the numeric type.
0
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2.15
Maximum skewness among attributes of the numeric type.
0.21
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.29
Third quartile of skewness among attributes of the numeric type.
0.67
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
2.51
First quartile of kurtosis among attributes of the numeric type.
0.45
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.
-0.03
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
5.57
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
-0.01
Mean of means among attributes of the numeric type.
-0.56
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.
0.29
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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