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Diabetes dataset

Describe the data, how we get here, references, how the features are collected...

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Ten baseline variables, age, sex, body mass index, average blood pressure, and six blood serum measurements were obtained for each of the 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

Attributes

• age: age in years
• sex: sex of participant
• 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)

Evaluation

Here we describe the target value, how they are obtained, how we evaluate it, other requirements...

The target value is a quantitative measure of disease progression one year after baseline.

The evaluation metric is the root mean squared error (RMSE) between the predicted value and the true value.