Data
MiceProtein

MiceProtein

in_preparation ARFF Publicly available Visibility: public Uploaded 08-11-2017 by Joaquin Vanschoren
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TBA

82 features

class (target)nominal8 unique values
0 missing
MouseID (row identifier)nominal1080 unique values
0 missing
DYRK1A_Nnumeric1077 unique values
3 missing
ITSN1_Nnumeric1076 unique values
3 missing
BDNF_Nnumeric1077 unique values
3 missing
NR1_Nnumeric1077 unique values
3 missing
NR2A_Nnumeric1077 unique values
3 missing
pAKT_Nnumeric1076 unique values
3 missing
pBRAF_Nnumeric1075 unique values
3 missing
pCAMKII_Nnumeric1077 unique values
3 missing
pCREB_Nnumeric1077 unique values
3 missing
pELK_Nnumeric1077 unique values
3 missing
pERK_Nnumeric1077 unique values
3 missing
pJNK_Nnumeric1076 unique values
3 missing
PKCA_Nnumeric1077 unique values
3 missing
pMEK_Nnumeric1077 unique values
3 missing
pNR1_Nnumeric1077 unique values
3 missing
pNR2A_Nnumeric1077 unique values
3 missing
pNR2B_Nnumeric1077 unique values
3 missing
pPKCAB_Nnumeric1077 unique values
3 missing
pRSK_Nnumeric1077 unique values
3 missing
AKT_Nnumeric1077 unique values
3 missing
BRAF_Nnumeric1077 unique values
3 missing
CAMKII_Nnumeric1077 unique values
3 missing
CREB_Nnumeric1073 unique values
3 missing
ELK_Nnumeric1062 unique values
18 missing
ERK_Nnumeric1077 unique values
3 missing
GSK3B_Nnumeric1077 unique values
3 missing
JNK_Nnumeric1077 unique values
3 missing
MEK_Nnumeric1072 unique values
7 missing
TRKA_Nnumeric1075 unique values
3 missing
RSK_Nnumeric1074 unique values
3 missing
APP_Nnumeric1077 unique values
3 missing
Bcatenin_Nnumeric1062 unique values
18 missing
SOD1_Nnumeric1077 unique values
3 missing
MTOR_Nnumeric1077 unique values
3 missing
P38_Nnumeric1075 unique values
3 missing
pMTOR_Nnumeric1077 unique values
3 missing
DSCR1_Nnumeric1077 unique values
3 missing
AMPKA_Nnumeric1075 unique values
3 missing
NR2B_Nnumeric1077 unique values
3 missing
pNUMB_Nnumeric1077 unique values
3 missing
RAPTOR_Nnumeric1077 unique values
3 missing
TIAM1_Nnumeric1075 unique values
3 missing
pP70S6_Nnumeric1076 unique values
3 missing
NUMB_Nnumeric1080 unique values
0 missing
P70S6_Nnumeric1080 unique values
0 missing
pGSK3B_Nnumeric1080 unique values
0 missing
pPKCG_Nnumeric1080 unique values
0 missing
CDK5_Nnumeric1080 unique values
0 missing
S6_Nnumeric1080 unique values
0 missing
ADARB1_Nnumeric1080 unique values
0 missing
AcetylH3K9_Nnumeric1080 unique values
0 missing
RRP1_Nnumeric1080 unique values
0 missing
BAX_Nnumeric1080 unique values
0 missing
ARC_Nnumeric1080 unique values
0 missing
ERBB4_Nnumeric1079 unique values
0 missing
nNOS_Nnumeric1079 unique values
0 missing
Tau_Nnumeric1080 unique values
0 missing
GFAP_Nnumeric1079 unique values
0 missing
GluR3_Nnumeric1080 unique values
0 missing
GluR4_Nnumeric1079 unique values
0 missing
IL1B_Nnumeric1080 unique values
0 missing
P3525_Nnumeric1080 unique values
0 missing
pCASP9_Nnumeric1080 unique values
0 missing
PSD95_Nnumeric1080 unique values
0 missing
SNCA_Nnumeric1079 unique values
0 missing
Ubiquitin_Nnumeric1080 unique values
0 missing
pGSK3B_Tyr216_Nnumeric1080 unique values
0 missing
SHH_Nnumeric1080 unique values
0 missing
BAD_Nnumeric866 unique values
213 missing
BCL2_Nnumeric795 unique values
285 missing
pS6_Nnumeric1080 unique values
0 missing
pCFOS_Nnumeric1005 unique values
75 missing
SYP_Nnumeric1079 unique values
0 missing
H3AcK18_Nnumeric900 unique values
180 missing
EGR1_Nnumeric870 unique values
210 missing
H3MeK4_Nnumeric810 unique values
270 missing
CaNA_Nnumeric1080 unique values
0 missing
Genotypenominal2 unique values
0 missing
Treatmentnominal2 unique values
0 missing
Behaviornominal2 unique values
0 missing

62 properties

1080
Number of instances (rows) of the dataset.
82
Number of attributes (columns) of the dataset.
8
Number of distinct values of the target attribute (if it is nominal).
1396
Number of missing values in the dataset.
528
Number of instances with at least one value missing.
77
Number of numeric attributes.
5
Number of nominal attributes.
2.99
Entropy of the target attribute values.
0
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Second quartile (Median) of entropy among attributes.
0.08
Number of attributes divided by the number of instances.
3.5
Average number of distinct values among the attributes of the nominal type.
0.89
Second quartile (Median) of kurtosis among attributes of the numeric type.
3
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.79
Mean skewness among attributes of the numeric type.
0.38
Second quartile (Median) of means among attributes of the numeric type.
13.89
Percentage of instances belonging to the most frequent class.
0.16
Mean standard deviation of attributes of the numeric type.
1
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
150
Number of instances belonging to the most frequent class.
1
Minimal entropy among attributes.
0.46
Second quartile (Median) of skewness among attributes of the numeric type.
1
Maximum entropy among attributes.
-0.74
Minimum kurtosis among attributes of the numeric type.
3.66
Percentage of binary attributes.
0.07
Second quartile (Median) of standard deviation of attributes of the numeric type.
62.55
Maximum kurtosis among attributes of the numeric type.
0.12
Minimum of means among attributes of the numeric type.
48.89
Percentage of instances having missing values.
1
Third quartile of entropy among attributes.
3.84
Maximum of means among attributes of the numeric type.
1
Minimal mutual information between the nominal attributes and the target attribute.
1.58
Percentage of missing values.
2.29
Third quartile of kurtosis among attributes of the numeric type.
1
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
93.9
Percentage of numeric attributes.
0.79
Third quartile of means among attributes of the numeric type.
8
The maximum number of distinct values among attributes of the nominal type.
-0.88
Minimum skewness among attributes of the numeric type.
6.1
Percentage of nominal attributes.
1
Third quartile of mutual information between the nominal attributes and the target attribute.
4.74
Maximum skewness among attributes of the numeric type.
0.01
Minimum standard deviation of attributes of the numeric type.
1
First quartile of entropy among attributes.
0.97
Third quartile of skewness among attributes of the numeric type.
1.3
Maximum standard deviation of attributes of the numeric type.
9.72
Percentage of instances belonging to the least frequent class.
0.21
First quartile of kurtosis among attributes of the numeric type.
0.23
Third quartile of standard deviation of attributes of the numeric type.
1
Average entropy of the attributes.
105
Number of instances belonging to the least frequent class.
0.19
First quartile of means among attributes of the numeric type.
3
Standard deviation of the number of distinct values among attributes of the nominal type.
4.54
Mean kurtosis among attributes of the numeric type.
3
Number of binary attributes.
1
First quartile of mutual information between the nominal attributes and the target attribute.
0.67
Mean of means among attributes of the numeric type.
0.03
First quartile of skewness among attributes of the numeric type.
0.99
Average class difference between consecutive instances.
1
Average mutual information between the nominal attributes and the target attribute.
0.03
First quartile of standard deviation of attributes of the numeric type.

18 tasks

0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - 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
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