Example01:利用PolynomialFeatures解决过拟合问题 (opens new window)

• 线性回归模型实在是太简单了，非常容易导致欠拟合，根据前文中解决欠拟合的手段一共有两个：

  • 增加物理特征，如房屋的地段，房屋的朝向等
  • 增加无意义的数学变量。使用sklearn.preprocessing.PolynomialFeatures

  from sklearn.linear_model import LinearRegression
  from sklearn.preprocessing import PolynomialFeatures
  from sklearn.pipeline import Pipeline
  
  def polynomial_model(degree=1):
      polynomial_features = PolynomialFeatures(degree=degree,
                                               include_bias=False)
      linear_regression = LinearRegression(normalize=True)
      pipeline = Pipeline([("polynomial_features", polynomial_features),
                           ("linear_regression", linear_regression)])
      return pipeline
  

  注：若是Pipeline中的若自己编写的函数，则默认调用的是.fit_transform方法，而最后的结点则必须要用.fit()实现。

• 使用mean_squared_error计算均方根误差

  from sklearn.metrics import mean_squared_error
  

  

Eample02：房价预测 (opens new window)

from sklearn import datasets
boston = datasets.load_boston(return_X_y=False)
X = boston.data
y = boston.target

# 或者直接元组接受
X, y = load_boston(return_X_y=True)