机器学习——线性回归模型的可解释性

    import numpy as np
    from sklearn import datasets
    
    #加载sklearn-datasets数据集中的波士顿房产数据
    boston = datasets.load_boston()
    
    X = boston.data #所有特征数据
    y = boston.target  #输出标记
    
    #绘制二维的散点图会发现数据中存在一些垃圾数据，将其清除
    X = X[y < 50.0]
    y = y[y < 50.0]
    
    #从sklearn中导入线性回归算法的package
    from sklearn.linear_model import LinearRegression
    
    lin_reg = LinearRegression() #实例化
    lin_reg.fit(X, y) #fit拟合操作
    
    #此时我们查看模型的参数coef_
    #关于模型中的 coef_ 和 interception_ 参数是什么，传送门：https://www.jianshu.com/p/6a818b53a37e
    In[1]: lin_reg.coef_
    Out[1]:array([-1.06715912e-01,  3.53133180e-02, -4.38830943e-02,  4.52209315e-01,
       -1.23981083e+01,  3.75945346e+00, -2.36790549e-02, -1.21096549e+00,
        2.51301879e-01, -1.37774382e-02, -8.38180086e-01,  7.85316354e-03,
       -3.50107918e-01])
       
    In[2]:np.argsort(lin_reg.coef_)   #对coef_进行索引排序
    Out[2]:array([ 4,  7, 10, 12,  0,  2,  6,  9, 11,  1,  8,  3,  5])
    
    In[3]:boston.feature_names  #查看波士顿房产数据中特征对应的名称
    Out[3]：array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',
       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')
       
    In[4]:boston.feature_names[np.argsort(lin_reg.coef_)]  #对特征名也进行上面一样的索引排序
    Out[4]:array(['NOX', 'DIS', 'PTRATIO', 'LSTAT', 'CRIM', 'INDUS', 'AGE', 'TAX',
       'B', 'ZN', 'RAD', 'CHAS', 'RM'], dtype='<U7')
       
    In[5]:print(boston.DESCR)  #我们输出数据的文档，查看每个特征名对应的意义
    Out[5]:.. _boston_dataset:
    
    Boston house prices dataset
    ---------------------------
    
    **Data Set Characteristics:**  
    
    :Number of Instances: 506 
    
    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.
    
    :Attribute Information (in order):
        - CRIM     per capita crime rate by town
        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.
        - INDUS    proportion of non-retail business acres per town
        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
        - NOX      nitric oxides concentration (parts per 10 million)
        - RM       average number of rooms per dwelling
        - AGE      proportion of owner-occupied units built prior to 1940
        - DIS      weighted distances to five Boston employment centres
        - RAD      index of accessibility to radial highways
        - TAX      full-value property-tax rate per $10,000
        - PTRATIO  pupil-teacher ratio by town
        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
        - LSTAT    % lower status of the population
        - MEDV     Median value of owner-occupied homes in $1000's
    
    :Missing Attribute Values: None
    
    :Creator: Harrison, D. and Rubinfeld, D.L.
    
    This is a copy of UCI ML housing dataset.
    https://archive.ics.uci.edu/ml/machine-learning-databases/housing/
    
    
    This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.
    
    The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic
    prices and the demand for clean air', J. Environ. Economics & Management,
    vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics
    ...', Wiley, 1980.   N.B. Various transformations are used in the table on
    pages 244-261 of the latter.
    
    The Boston house-price data has been used in many machine learning papers that address regression
    problems.   
     
    .. topic:: References
    
       - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.
       - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.