:py:mod:`sklearn_pmml_model.linear_model`
=========================================

.. py:module:: sklearn_pmml_model.linear_model

.. autoapi-nested-parse::

   
   The :mod:`sklearn_pmml_model.linear_model` module implements generalized linear models.
















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Submodules
----------
.. toctree::
   :titlesonly:
   :maxdepth: 1

   base/index.rst
   implementations/index.rst


Package Contents
----------------

Classes
~~~~~~~

.. autoapisummary::

   sklearn_pmml_model.linear_model.PMMLLinearRegression
   sklearn_pmml_model.linear_model.PMMLLogisticRegression
   sklearn_pmml_model.linear_model.PMMLRidge
   sklearn_pmml_model.linear_model.PMMLRidgeClassifier
   sklearn_pmml_model.linear_model.PMMLLasso
   sklearn_pmml_model.linear_model.PMMLElasticNet




.. py:class:: PMMLLinearRegression(pmml)


   Bases: :py:obj:`sklearn_pmml_model.base.OneHotEncodingMixin`, :py:obj:`sklearn_pmml_model.base.PMMLBaseRegressor`, :py:obj:`sklearn.linear_model.LinearRegression`

   
   Ordinary least squares Linear Regression.

   The PMML model consists out of a <RegressionModel> element, containing at
   least one <RegressionTable> element. Every table element contains a
   <NumericPredictor> element for numerical fields and <CategoricalPredictor>
   per value of a categorical field, describing the coefficients.

   :Parameters:

       **pmml** : str, object
           Filename or file object containing PMML data.









   .. rubric:: Notes

   Specification: http://dmg.org/pmml/v4-3/Regression.html





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   .. py:method:: fit(x, y)

      
      Not supported: PMML models are already fitted.
















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.. py:class:: PMMLLogisticRegression(pmml)


   Bases: :py:obj:`sklearn_pmml_model.base.OneHotEncodingMixin`, :py:obj:`sklearn_pmml_model.base.PMMLBaseClassifier`, :py:obj:`sklearn.linear_model.LogisticRegression`

   
   Logistic Regression (aka logit, MaxEnt) classifier.

   The PMML model consists out of a <RegressionModel> element, containing at
   least one <RegressionTable> element. Every table element contains a
   <NumericPredictor> element for numerical fields and <CategoricalPredictor>
   per value of a categorical field, describing the coefficients.

   :Parameters:

       **pmml** : str, object
           Filename or file object containing PMML data.









   .. rubric:: Notes

   Specification: http://dmg.org/pmml/v4-3/Regression.html





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   .. py:method:: fit(x, y)

      
      Not supported: PMML models are already fitted.
















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.. py:class:: PMMLRidge(alpha=1.0, *, fit_intercept=True, copy_X=True, max_iter=None, tol=0.0001, solver='auto', positive=False, random_state=None)


   Bases: :py:obj:`sklearn_pmml_model.linear_model.base.PMMLGeneralizedLinearRegressor`, :py:obj:`sklearn.linear_model.Ridge`

   
   Linear least squares with l2 regularization.

   Minimizes the objective function::

   ||y - Xw||^2_2 + alpha * ||w||^2_2

   This model solves a regression model where the loss function is
   the linear least squares function and regularization is given by
   the l2-norm. Also known as Ridge Regression or Tikhonov regularization.
   This estimator has built-in support for multi-variate regression
   (i.e., when y is a 2d-array of shape (n_samples, n_targets)).

   :Parameters:

       **pmml** : str, object
           Filename or file object containing PMML data.









   .. rubric:: Notes

   Specification: http://dmg.org/pmml/v4-3/GeneralRegression.html





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   .. py:method:: fit(x, y)

      
      Fit Ridge regression model.


      :Parameters:

          **X** : {ndarray, sparse matrix} of shape (n_samples, n_features)
              Training data.

          **y** : ndarray of shape (n_samples,) or (n_samples, n_targets)
              Target values.

          **sample_weight** : float or ndarray of shape (n_samples,), default=None
              Individual weights for each sample. If given a float, every sample
              will have the same weight.

      :Returns:

          **self** : object
              Fitted estimator.













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.. py:class:: PMMLRidgeClassifier(pmml)


   Bases: :py:obj:`sklearn_pmml_model.linear_model.base.PMMLGeneralizedLinearClassifier`, :py:obj:`sklearn.linear_model.RidgeClassifier`

   
   Classifier using Ridge regression.

   This classifier first converts the target values into ``{-1, 1}`` and
   then treats the problem as a regression task (multi-output regression in
   the multiclass case).

   :Parameters:

       **pmml** : str, object
           Filename or file object containing PMML data.









   .. rubric:: Notes

   Specification: http://dmg.org/pmml/v4-3/GeneralRegression.html





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   .. py:method:: fit(x, y)

      
      Fit Ridge classifier model.


      :Parameters:

          **X** : {ndarray, sparse matrix} of shape (n_samples, n_features)
              Training data.

          **y** : ndarray of shape (n_samples,)
              Target values.

          **sample_weight** : float or ndarray of shape (n_samples,), default=None
              Individual weights for each sample. If given a float, every sample
              will have the same weight.
              
              .. versionadded:: 0.17
                 *sample_weight* support to RidgeClassifier.

      :Returns:

          **self** : object
              Instance of the estimator.













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.. py:class:: PMMLLasso(pmml)


   Bases: :py:obj:`sklearn_pmml_model.linear_model.base.PMMLGeneralizedLinearRegressor`, :py:obj:`sklearn.linear_model.Lasso`

   
   Linear Model trained with L1 prior as regularizer (aka the Lasso).

   The optimization objective for Lasso is::

       (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1

   Technically the Lasso model is optimizing the same objective function as
   the Elastic Net with ``l1_ratio=1.0`` (no L2 penalty).

   :Parameters:

       **pmml** : str, object
           Filename or file object containing PMML data.









   .. rubric:: Notes

   Specification: http://dmg.org/pmml/v4-3/GeneralRegression.html





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   .. py:method:: fit(x, y)

      
      Fit model with coordinate descent.


      :Parameters:

          **X** : {ndarray, sparse matrix} of (n_samples, n_features)
              Data.

          **y** : ndarray of shape (n_samples,) or (n_samples, n_targets)
              Target. Will be cast to X's dtype if necessary.

          **sample_weight** : float or array-like of shape (n_samples,), default=None
              Sample weights. Internally, the `sample_weight` vector will be
              rescaled to sum to `n_samples`.
              
              .. versionadded:: 0.23

          **check_input** : bool, default=True
              Allow to bypass several input checking.
              Don't use this parameter unless you know what you do.

      :Returns:

          **self** : object
              Fitted estimator.








      .. rubric:: Notes

      Coordinate descent is an algorithm that considers each column of
      data at a time hence it will automatically convert the X input
      as a Fortran-contiguous numpy array if necessary.

      To avoid memory re-allocation it is advised to allocate the
      initial data in memory directly using that format.





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.. py:class:: PMMLElasticNet(pmml)


   Bases: :py:obj:`sklearn_pmml_model.linear_model.base.PMMLGeneralizedLinearRegressor`, :py:obj:`sklearn.linear_model.ElasticNet`

   
   Linear regression with combined L1 and L2 priors as regularizer.

   Minimizes the objective function::

           1 / (2 * n_samples) * ||y - Xw||^2_2
           + alpha * l1_ratio * ||w||_1
           + 0.5 * alpha * (1 - l1_ratio) * ||w||^2_2

   If you are interested in controlling the L1 and L2 penalty
   separately, keep in mind that this is equivalent to::

           a * ||w||_1 + 0.5 * b * ||w||_2^2

   where::

           alpha = a + b and l1_ratio = a / (a + b)

   The parameter l1_ratio corresponds to alpha in the glmnet R package while
   alpha corresponds to the lambda parameter in glmnet. Specifically, l1_ratio
   = 1 is the lasso penalty. Currently, l1_ratio <= 0.01 is not reliable,
   unless you supply your own sequence of alpha.

   :Parameters:

       **pmml** : str, object
           Filename or file object containing PMML data.









   .. rubric:: Notes

   Specification: http://dmg.org/pmml/v4-3/GeneralRegression.html





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   .. py:method:: fit(x, y)

      
      Fit model with coordinate descent.


      :Parameters:

          **X** : {ndarray, sparse matrix} of (n_samples, n_features)
              Data.

          **y** : ndarray of shape (n_samples,) or (n_samples, n_targets)
              Target. Will be cast to X's dtype if necessary.

          **sample_weight** : float or array-like of shape (n_samples,), default=None
              Sample weights. Internally, the `sample_weight` vector will be
              rescaled to sum to `n_samples`.
              
              .. versionadded:: 0.23

          **check_input** : bool, default=True
              Allow to bypass several input checking.
              Don't use this parameter unless you know what you do.

      :Returns:

          **self** : object
              Fitted estimator.








      .. rubric:: Notes

      Coordinate descent is an algorithm that considers each column of
      data at a time hence it will automatically convert the X input
      as a Fortran-contiguous numpy array if necessary.

      To avoid memory re-allocation it is advised to allocate the
      initial data in memory directly using that format.





      ..
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   .. py:method:: _more_tags()