Package parsimony :: Module estimators :: Class RegressionEstimator
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Class RegressionEstimator

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   object --+    
            |    
BaseEstimator --+
                |
               RegressionEstimator
Known Subclasses:

Base estimator for regression estimation.

Parameters
----------
algorithm : ExplicitAlgorithm. The algorithm that will be applied.

start_vector : BaseStartVector. Generates the start vector that will be
        used.

Instance Methods [hide private]
 
__init__(self, algorithm, start_vector=start_vectors.RandomStartVector())
x.__init__(...) initializes x; see help(type(x)) for signature
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fit(self, X, y)
Fit the estimator to the data.
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predict(self, X)
Perform prediction using the fitted parameters.
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score(self, X, y)
Return the score of the estimator.
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Inherited from BaseEstimator: get_info, get_params, parameters, set_params

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__

Class Variables [hide private]
  __metaclass__ = abc.ABCMeta
Metaclass for defining Abstract Base Classes (ABCs).
  __abstractmethods__ = frozenset(['fit', 'get_params', 'score'])
Properties [hide private]

Inherited from object: __class__

Method Details [hide private]

__init__(self, algorithm, start_vector=start_vectors.RandomStartVector())
(Constructor)

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x.__init__(...) initializes x; see help(type(x)) for signature

Overrides: object.__init__
(inherited documentation)

fit(self, X, y)

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Fit the estimator to the data.

Decorators:
  • @abc.abstractmethod
Overrides: BaseEstimator.fit

predict(self, X)

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Perform prediction using the fitted parameters.

Overrides: BaseEstimator.predict

score(self, X, y)

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Return the score of the estimator.

The score is a measure of "goodness" of the fit to the data.

Decorators:
  • @abc.abstractmethod
Overrides: BaseEstimator.score