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Diabetes(scikit-learn)

Diabetes(scikit-learn)

active ARFF BSD (from scikit-learn) Visibility: public Uploaded 26-08-2022 by Andrea
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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 age in years - sex - bmi body mass index - bp average blood pressure - s1 tc, total serum cholesterol - s2 ldl, low-density lipoproteins - s3 hdl, high-density lipoproteins - s4 tch, total cholesterol / HDL - s5 ltg, possibly log of serum triglycerides level - s6 glu, blood sugar level Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times the square root of `n_samples` (i.e. the sum of squares of each column totals 1). Source URL: https://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. (https://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

19 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
Percentage of nominal attributes.
-84.64
Average class difference between consecutive instances.
100
Percentage of numeric attributes.
0
Percentage of missing values.
0
Percentage of instances having missing values.
0
Percentage of binary attributes.
0
Number of binary attributes.
Number of instances belonging to the least frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the most frequent class.
0.02
Number of attributes divided by the number of instances.

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