如何对sklearn糖尿病数据集进行非规范化处理？

Question

There is a nice example of linear regression in sklearn using a diabetes dataset.

I copied the notebook version and played with it a bit in Jupyterlab. Of course, it works just like the example. But I wondered what I was really seeing.

• There is a chart with unlabeled axes.
• I wondered what the label (dependent variable) was.
• I wondered which of the 10 independent variables was being used.

So I played around with the nice features provided by ipython/jupyter:

diabetes.DESCR

Diabetes dataset
================
Notes
-----
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
:Attributes:
: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)'

From the Source URL, we are led to the original raw data which is a tab-separated unnormalized copy of the data. It also further explains what the "S" features were in the problem domain.

• Interestingly, sex was one of [1,2] with a guess as to what they meant.

But my real question is whether there is a way within sklearn to determine

• how to denormalize the data in sklearn?
• Is there a way to denormalize the coefficients and intercept so that one could express the fit algebraically?

or is this just a demonstration of linear regression?

Answer 1

There is no way to denormalize data without any information about the data prior to the normalization. However, note that the sklearn.preprocessing classes MinMaxScaler , StandardScaler , etc. do include inverse_transform methods ( example ), so if this were also provided in the example it would be easy to do. As it stands, as you say, this is just a regression demonstration.