PolynomialFeatures

sklearn.preprocessing.PolynomialFeatures 用法
sklearn中数据预处理过程中的多项式特征构造工具包。
eg:[x1,x2] -> [1,x1,x2,x1^2,x1*x2,x22]输入一个二维样本。产生二阶多项式特征。

1.参数理解：（一共只有３个参数）

• degree : integer
  The degree of the polynomial features. Default = 2.
  多项式的阶数，一般默认是２。

• interaction_only : boolean, default = False
  If true, only interaction features are produced: features that are products of at most degree distinct input features (so not x[1] ** 2, x[0] * x[2] ** 3, etc.).
  如果值为true(默认是false),则会产生相互影响的特征集。

• include_bias : boolean
  If True (default), then include a bias column, the feature in which all polynomial powers are zero (i.e. a column of ones - acts as an intercept term in a linear model).
  是否包含偏差列

2.属性

• powers_ : array, shape (n_input_features, n_output_features)
  powers_[i, j] is the exponent of the jth input in the ith output.

• n_input_features_ : int
  The total number of input features.
  输入特征的个数

• n_output_features_ : int
  The total number of polynomial output features. The number of output features is computed by iterating over all suitably sized combinations of input features.
  输出多项式的特征个数。它的计算是通过遍历所有的适当大小的输入特征组合。

3.方法

1. fit(X, y=None)
   Compute number of output features.
   计算输出特征的个数
2. fit_transform(X, y=None, **fit_params)
   Fit to data, then transform it.