Class TheilSenRegressorScikitsLearnNode

Theil-Sen Estimator: robust multivariate regression model.

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

The algorithm calculates least square solutions on subsets with size
n_subsamples of the samples in X. Any value of n_subsamples between the
number of features and samples leads to an estimator with a compromise
between robustness and efficiency. Since the number of least square
solutions is "n_samples choose n_subsamples", it can be extremely large
and can therefore be limited with max_subpopulation. If this limit is
reached, the subsets are chosen randomly. In a final step, the spatial
median (or L1 median) is calculated of all least square solutions.

Read more in the :ref:`User Guide <theil_sen_regression>`.

**Parameters**

fit_intercept : boolean, optional, default True
    Whether to calculate the intercept for this model. If set
    to false, no intercept will be used in calculations.

copy_X : boolean, optional, default True
    If True, X will be copied; else, it may be overwritten.

max_subpopulation : int, optional, default 1e4
    Instead of computing with a set of cardinality 'n choose k', where n is
    the number of samples and k is the number of subsamples (at least
    number of features), consider only a stochastic subpopulation of a
    given maximal size if 'n choose k' is larger than max_subpopulation.
    For other than small problem sizes this parameter will determine
    memory usage and runtime if n_subsamples is not changed.

n_subsamples : int, optional, default None
    Number of samples to calculate the parameters. This is at least the
    number of features (plus 1 if fit_intercept=True) and the number of
    samples as a maximum. A lower number leads to a higher breakdown
    point and a low efficiency while a high number leads to a low
    breakdown point and a high efficiency. If None, take the
    minimum number of subsamples leading to maximal robustness.
    If n_subsamples is set to n_samples, Theil-Sen is identical to least
    squares.

max_iter : int, optional, default 300
    Maximum number of iterations for the calculation of spatial median.

tol : float, optional, default 1.e-3
    Tolerance when calculating spatial median.

random_state : RandomState or an int seed, optional, default None
    A random number generator instance to define the state of the
    random permutations generator.

n_jobs : integer, optional, default 1
    Number of CPUs to use during the cross validation. If ``-1``, use
    all the CPUs.

verbose : boolean, optional, default False
    Verbose mode when fitting the model.

**Attributes**

``coef_`` : array, shape = (n_features)
    Coefficients of the regression model (median of distribution).

``intercept_`` : float
    Estimated intercept of regression model.

``breakdown_`` : float
    Approximated breakdown point.

``n_iter_`` : int
    Number of iterations needed for the spatial median.

``n_subpopulation_`` : int
    Number of combinations taken into account from 'n choose k', where n is
    the number of samples and k is the number of subsamples.

**References**

- Theil-Sen Estimators in a Multiple Linear Regression Model, 2009
  Xin Dang, Hanxiang Peng, Xueqin Wang and Heping Zhang
  http://www.math.iupui.edu/~hpeng/MTSE_0908.pdf

Overrides: object.__init__

Predict using the linear model

This node has been automatically generated by wrapping the sklearn.linear_model.theil_sen.TheilSenRegressor class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix}, shape = (n_samples, n_features)

Samples.

Returns

C : array, shape = (n_samples,)

Returns predicted values.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit linear model.

This node has been automatically generated by wrapping the sklearn.linear_model.theil_sen.TheilSenRegressor class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : numpy array of shape [n_samples, n_features]

Training data

y : numpy array of shape [n_samples]

Target values

Returns

self : returns an instance of self.

Overrides: Node.stop_training