Class LinearDiscriminantAnalysisScikitsLearnNode

Linear Discriminant Analysis

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

A classifier with a linear decision boundary, generated by fitting class
conditional densities to the data and using Bayes' rule.

The model fits a Gaussian density to each class, assuming that all classes
share the same covariance matrix.

The fitted model can also be used to reduce the dimensionality of the input
by projecting it to the most discriminative directions.

.. versionadded:: 0.17
   *LinearDiscriminantAnalysis*.

.. versionchanged:: 0.17
   Deprecated :class:`lda.LDA` have been moved to *LinearDiscriminantAnalysis*.

**Parameters**

solver : string, optional
    Solver to use, possible values:

      - - 'svd': Singular value decomposition (default). Does not compute the
      -       covariance matrix, therefore this solver is recommended for
      -       data with a large number of features.
      - - 'lsqr': Least squares solution, can be combined with shrinkage.
      - - 'eigen': Eigenvalue decomposition, can be combined with shrinkage.


shrinkage : string or float, optional
    Shrinkage parameter, possible values:

      - - None: no shrinkage (default).
      - - 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
      - - float between 0 and 1: fixed shrinkage parameter.


    Note that shrinkage works only with 'lsqr' and 'eigen' solvers.

priors : array, optional, shape (n_classes,)
    Class priors.

n_components : int, optional
    Number of components (< n_classes - 1) for dimensionality reduction.

store_covariance : bool, optional
    Additionally compute class covariance matrix (default False).

    .. versionadded:: 0.17

tol : float, optional
    Threshold used for rank estimation in SVD solver.

    .. versionadded:: 0.17

**Attributes**

``coef_`` : array, shape (n_features,) or (n_classes, n_features)
    Weight vector(s).

``intercept_`` : array, shape (n_features,)
    Intercept term.

``covariance_`` : array-like, shape (n_features, n_features)
    Covariance matrix (shared by all classes).

``explained_variance_ratio_`` : array, shape (n_components,)
    Percentage of variance explained by each of the selected components.
    If ``n_components`` is not set then all components are stored and the
    sum of explained variances is equal to 1.0. Only available when eigen
    solver is used.

``means_`` : array-like, shape (n_classes, n_features)
    Class means.

``priors_`` : array-like, shape (n_classes,)
    Class priors (sum to 1).

``scalings_`` : array-like, shape (rank, n_classes - 1)
    Scaling of the features in the space spanned by the class centroids.

``xbar_`` : array-like, shape (n_features,)
    Overall mean.

``classes_`` : array-like, shape (n_classes,)
    Unique class labels.

See also

sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis: Quadratic
    Discriminant Analysis

**Notes**

The default solver is 'svd'. It can perform both classification and
transform, and it does not rely on the calculation of the covariance
matrix. This can be an advantage in situations where the number of features
is large. However, the 'svd' solver cannot be used with shrinkage.

The 'lsqr' solver is an efficient algorithm that only works for
classification. It supports shrinkage.

The 'eigen' solver is based on the optimization of the between class
scatter to within class scatter ratio. It can be used for both
classification and transform, and it supports shrinkage. However, the
'eigen' solver needs to compute the covariance matrix, so it might not be
suitable for situations with a high number of features.

**Examples**

>>> import numpy as np
>>> from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> y = np.array([1, 1, 1, 2, 2, 2])
>>> clf = LinearDiscriminantAnalysis()
>>> clf.fit(X, y)
LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,
              solver='svd', store_covariance=False, tol=0.0001)
>>> print(clf.predict([[-0.8, -1]]))
[1]

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Predict class labels for samples in X.

This node has been automatically generated by wrapping the sklearn.discriminant_analysis.LinearDiscriminantAnalysis 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]

Predicted class label per sample.

Overrides: ClassifierNode.label

Fit LinearDiscriminantAnalysis model according to the given
training data and parameters.

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

   .. versionchanged:: 0.17
      Deprecated *store_covariance* have been moved to main constructor.

   .. versionchanged:: 0.17
      Deprecated *tol* have been moved to main constructor.

**Parameters**

X : array-like, shape (n_samples, n_features)
    Training data.

y : array, shape (n_samples,)
    Target values.

Overrides: Node.stop_training