基于模型（Model-based）进行特征选择(feature selection)并可视化特征重要性(feature importance)

Posted 2021-05-29 Data+Science+Insight

基于模型（Model-based）进行特征选择(feature selection)并可视化特征重要性(feature importance)
 

sklean 中的 SelectFromModel进行特征选择的基础是特征重要程度、即特种重要性。

 

# 加载sklearn自带的糖尿病（diabetes）数据集并输出数据集的说明信息，进行数据的理解

一共有422个样本，10个特征：

数据集包含、年龄、性别、BMI指标、血压、T细胞指标、低密度脂蛋白、高密度脂蛋白、甲状腺素、拉莫三嗪、血糖值；

其中血糖值是回归的目标；

from sklearn.datasets import load_diabetes

diabetes = load_diabetes()
X, y = diabetes.data, diabetes.target
print(diabetes.DESCR)

.. _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 Attri