Class PolynomialFeaturesScikitsLearnNode

Generate polynomial and interaction features.

This node has been automatically generated by wrapping the ``sklearn.preprocessing.data.PolynomialFeatures`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

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].

**Parameters**

degree : integer
    The degree of the polynomial features. Default = 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.).

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).

**Examples**

>>> 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.]])

**Attributes**

``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.

``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.

**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
<example_linear_model_plot_polynomial_interpolation.py>`

Overrides: object.__init__

Transform data to polynomial features

This node has been automatically generated by wrapping the sklearn.preprocessing.data.PolynomialFeatures class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like, shape [n_samples, n_features]

The data to transform, row by row.

Returns

XP : np.ndarray shape [n_samples, NP]

The matrix of features, where NP is the number of polynomial features generated from the combination of inputs.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Overrides: Node.stop_training