Init signature:
PolynomialFeatures(
degree=2,
*,
interaction_only=False,
include_bias=True,
order=‘C’,
)
Docstring:
Generate polynomial and interaction features.
Generate a new feature matrix consisting of all polynomial combinations
of the features with degree less than or equal to the specified degree.
For example, if an input sample is two dimensional and of the form
[a, b], the degree-2 polynomial features are [1, a, b, a^2, ab, b^2].
Read more in the :ref:
User Guide <polynomial_features>
.
Parameters
degree : int or tuple (min_degree, max_degree), default=2
If a single int is given, it specifies the maximal degree of the
polynomial features. If a tuple
(min_degree, max_degree)
is passed,
then
min_degree
is the minimum and
max_degree
is the maximum
polynomial degree of the generated features. Note that
min_degree=0
and
min_degree=1
are equivalent as outputting the degree zero term is
determined by
include_bias
.
interaction_only : bool, default=False
If
True
, only interaction features are produced: features that are
products of at most
degree
distinct
input features, i.e. terms with
power of 2 or higher of the same input feature are excluded:
- included: `x[0]`, `x[1]`, `x[0] * x[1]`, etc.
- excluded: `x[0] ** 2`, `x[0] ** 2 * x[1]`, etc.
include_bias : bool, default=True
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).
order : {‘C’, ‘F’}, default=‘C’
Order of output array in the dense case.
'F'
order is faster to
compute, but may slow down subsequent estimators.
.. versionadded:: 0.21
Attributes
powers_ : ndarray of shape (
n_output_features_
,
n_features_in_
)
powers_[i, j]
is the exponent of the jth input in the ith output.
n_input_features_ : int
The total number of input features.
.. deprecated:: 1.0
This attribute is deprecated in 1.0 and will be removed in 1.2.
Refer to `n_features_in_` instead.
n_features_in_ : int
Number of features seen during :term:
fit
.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (
n_features_in_
,)
Names of features seen during :term:
fit
. Defined only when
X
has feature names that are all strings.
.. versionadded:: 1.0
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.
See Also
SplineTransformer : Transformer that generates univariate B-spline bases
for features.
Notes
Be aware that the number of features in the output array scales
polynomially in the number of features of the input array, and
exponentially in the degree. High degrees can cause overfitting.
See :ref:
examples/linear_model/plot_polynomial_interpolation.py <sphx_glr_auto_examples_linear_model_plot_polynomial_interpolation.py>
Examples
import numpy as np
from sklearn.preprocessing import PolynomialFeatures
X = np.arange(6).reshape(3, 2)
X
array([[0, 1],
[2, 3],
[4, 5]])
poly = PolynomialFeatures(2)
poly.fit_transform(X)
array([[ 1., 0., 1., 0., 0., 1.],
[ 1., 2., 3., 4., 6., 9.],
[ 1., 4., 5., 16., 20., 25.]])
poly = PolynomialFeatures(interaction_only=True)
poly.fit_transform(X)
array([[ 1., 0., 1., 0.],
[ 1., 2., 3., 6.],
[ 1., 4., 5., 20.]])
File: e:\anaconda3\lib\site-packages\sklearn\preprocessing_polynomial.py
Type: type
Subclasses: