What is the Sklearn Diabetes Dataset?
The scikit-learn Diabetes Dataset or Sklearn Diabetes dataset consists of ten baseline variables, such as age, sex, body mass index (BMI), average blood pressure, and six blood serum measurements, obtained for 442 diabetes patients. The target variable is a quantitative measure of disease progression one year after baseline.
Sklearn Diabetes Dataset
Characteristics of Sklearn Diabetes Dataset
- Number of Instances: 442
- Number of Attributes: The first 10 columns are numeric predictive values.
- Target: Column 11 represents a quantitative measure of disease progression one year after baseline.
Attribute Information of Sklearn Diabetes Dataset
The Sklearn Diabetes Dataset include following attributes:
- age: Age in years
- sex: Gender of the patient
- bmi: Body mass index
- bp: Average blood pressure
- s1: Total serum cholesterol (tc)
- s2: Low-density lipoproteins (ldl)
- s3: High-density lipoproteins (hdl)
- s4: Total cholesterol / HDL (tch)
- s5: Possibly log of serum triglycerides level (ltg)
- s6: Blood sugar level (glu)
Sklearn Diabetes Dataset : Scikit-learn Toy Datasets in Python
The Sklearn Diabetes Dataset typically refers to a dataset included in the scikit-learn machine learning library, which is a synthetic dataset rather than real-world data. This dataset is often used for demonstration purposes in machine learning tutorials and examples. In this article, we are going to learn more about the Sklearn Diabetes Dataset, how to load the dataset, and its application in machine learning.