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Diabetes(scikit-learn)

Diabetes(scikit-learn)

active ARFF Public Domain (CC0) Visibility: public Uploaded 22-03-2019 by Joaquin Vanschoren
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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

62 properties

442
Number of instances (rows) of the dataset.
11
Number of attributes (columns) of the dataset.
0
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.
11
Number of numeric attributes.
0
Number of nominal attributes.
0
Third quartile of means 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.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
-0.23
Minimum skewness among attributes of the numeric type.
0
Percentage of nominal attributes.
0.6
Third quartile of skewness among attributes of the numeric type.
0.8
Maximum skewness among attributes of the numeric type.
0.05
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.05
Third quartile of standard deviation of attributes of the numeric type.
77.09
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.67
First quartile of kurtosis among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
-0
First quartile of means among attributes of the numeric type.
-0.15
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.
13.83
Mean of means among attributes of the numeric type.
0.21
First quartile of skewness among attributes of the numeric type.
-84.64
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.05
First quartile of standard deviation of attributes of the numeric type.
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.
0.02
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
0.1
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.
0.37
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.
7.05
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.
0.38
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.99
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.05
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
0.98
Maximum kurtosis among attributes of the numeric type.
-0
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
0.44
Third quartile of kurtosis among attributes of the numeric type.
152.13
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.

2 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
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