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Diabetes(scikit-learn)

Diabetes(scikit-learn)

active ARFF BSD (from scikit-learn) Visibility: public Uploaded 22-03-2019 by Felicia West
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.. _diabetes_dataset: Diabetes dataset ---------------- Ten baseline variables, age, sex, body mass index, average blood pressure, and six blood serum measurements were obtained for each of n = 442 diabetes patients, as well as the response of interest, a quantitative measure of disease progression one year after baseline. Data Set Characteristics: :Number of Instances: 442 :Number of Attributes: First 10 columns are numeric predictive values :Target: Column 11 is a quantitative measure of disease progression one year after baseline :Attribute Information: - Age - Sex - Body mass index - Average blood pressure - S1 - S2 - S3 - S4 - S5 - S6 Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1). Source URL: http://www4.stat.ncsu.edu/~boos/var.select/diabetes.html For more information see: Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) "Least Angle Regression," Annals of Statistics (with discussion), 407-499. (http://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)

11 features

class (target)numeric214 unique values
0 missing
agenumeric58 unique values
0 missing
sexnumeric2 unique values
0 missing
bminumeric163 unique values
0 missing
bpnumeric100 unique values
0 missing
s1numeric141 unique values
0 missing
s2numeric302 unique values
0 missing
s3numeric63 unique values
0 missing
s4numeric66 unique values
0 missing
s5numeric184 unique values
0 missing
s6numeric56 unique values
0 missing
Show all 11 features

62 properties

NumberOfInstances
442
Number of instances (rows) of the dataset.
NumberOfFeatures
11
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
11
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.67
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
-0
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
0.21
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.05
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.1
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
-0
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.38
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.05
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
0.44
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.6
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.05
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
-84.64
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
13.83
Mean of means among attributes of the numeric type.
ClassEntropy
Entropy of the target attribute values.
Dimensionality
0.02
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
0.98
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
152.13
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.8
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
77.09
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.15
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
0
Number of binary attributes.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.37
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
7.05
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.99
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-0
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.23
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.05
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
Show all 62 properties

13 tasks

Supervised Regression on Diabetes(scikit-learn)
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Regression on Diabetes(scikit-learn)
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Regression on Diabetes(scikit-learn)
0 runs - estimation_procedure: Custom 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Regression on Diabetes(scikit-learn)
0 runs - estimation_procedure: Test on Training Data - evaluation_measure: predictive_accuracy - target_feature: class
Clustering on Diabetes(scikit-learn)
0 runs - estimation_procedure: 50 times Clustering
Clustering on Diabetes(scikit-learn)
0 runs - estimation_procedure: 50 times Clustering
Clustering on Diabetes(scikit-learn)
0 runs - estimation_procedure: 50 times Clustering
Clustering on Diabetes(scikit-learn)
0 runs - estimation_procedure: 50 times Clustering
Clustering on Diabetes(scikit-learn)
0 runs - estimation_procedure: 50 times Clustering
Clustering on Diabetes(scikit-learn)
0 runs - estimation_procedure: 50 times Clustering
Clustering on Diabetes(scikit-learn)
0 runs - estimation_procedure: 50 times Clustering
Clustering on Diabetes(scikit-learn)
0 runs - estimation_procedure: 50 times Clustering
Clustering on Diabetes(scikit-learn)
0 runs - estimation_procedure: 50 times Clustering
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