@ -116,10 +116,10 @@ Import some libraries to help with your tasks.
The built-in [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) includes 442 samples of data around diabetes, with 10 feature variables, some of which include:
age: age in years
bmi: body mass index
bp: average blood pressure
s1 tc: T-Cells (a type of white blood cells)
- age: age in years
- bmi: body mass index
- bp: average blood pressure
- s1 tc: T-Cells (a type of white blood cells)
✅ This dataset includes the concept of 'sex' as a feature variable important to research around diabetes. Many medical datasets include this type of binary classification. Think a bit about how categorizations such as this might exclude certain parts of a population from treatments.
@ -115,10 +115,10 @@ Scikit-learn 사용하면 올바르게 모델을 만들고 사용하기 위해
빌트-인된 [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset)은 당뇨에 대한 442개의 데이터 샘플이 있고, 10개의 feature 변수가 있으며, 그 일부는 아래와 같습니다:
age: age in years
bmi: body mass index
bp: average blood pressure
s1 tc: T-Cells (a type of white blood cells)
- age: age in years
- bmi: body mass index
- bp: average blood pressure
- s1 tc: T-Cells (a type of white blood cells)
✅ 이 데이터셋에는 당뇨를 연구할 때 중요한 feature 변수인 '성' 컨셉이 포함되어 있습니다. 많은 의학 데이터셋에는 binary classification의 타입이 포함됩니다. 이처럼 categorizations이 치료에서 인구의 특정 파트를 제외할 수 있는 방법에 대하여 조금 고민해보세요.
> 你可以在[Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html)网站上观察这些值的计算方法。另请访问[这个最小二乘计算器](https://www.mathsisfun.com/data/least-squares-calculator.html)以观察数字的值如何影响直线。
> 你可以在[Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html)网站上观察这些值的计算方法。另请访问[这个最小二乘计算器](https://www.mathsisfun.com/data/least-squares-calculator.html)以观察数字的值如何影响直线。