class sklearn.discriminant_analysis.LinearDiscriminantAnalysis(solver=’svd’, shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001)
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Linear Discriminant Analysis
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.
New in version 0.17: LinearDiscriminantAnalysis.
Read more in the User Guide.
Parameters: 


Attributes: 

See also
sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis
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.
>>> 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]
decision_function (X)  Predict confidence scores for samples. 
fit (X, y)  Fit LinearDiscriminantAnalysis model according to the given training data and parameters. 
fit_transform (X[, y])  Fit to data, then transform it. 
get_params ([deep])  Get parameters for this estimator. 
predict (X)  Predict class labels for samples in X. 
predict_log_proba (X)  Estimate log probability. 
predict_proba (X)  Estimate probability. 
score (X, y[, sample_weight])  Returns the mean accuracy on the given test data and labels. 
set_params (**params)  Set the parameters of this estimator. 
transform (X)  Project data to maximize class separation. 
__init__(solver=’svd’, shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001)
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decision_function(X)
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Predict confidence scores for samples.
The confidence score for a sample is the signed distance of that sample to the hyperplane.
Parameters: 


Returns: 

fit(X, y)
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training data and parameters.
Changed in version 0.19: store_covariance has been moved to main constructor.
Changed in version 0.19: tol has been moved to main constructor.
Parameters: 


fit_transform(X, y=None, **fit_params)
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Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters: 


Returns: 

get_params(deep=True)
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Get parameters for this estimator.
Parameters: 


Returns: 

predict(X)
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Predict class labels for samples in X.
Parameters: 


Returns: 

predict_log_proba(X)
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Estimate log probability.
Parameters: 


Returns: 

predict_proba(X)
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Estimate probability.
Parameters: 


Returns: 

score(X, y, sample_weight=None)
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Returns the mean accuracy on the given test data and labels.
In multilabel classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters: 


Returns: 

set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
Returns: 


transform(X)
[source]
Project data to maximize class separation.
Parameters: 


Returns: 

sklearn.discriminant_analysis.LinearDiscriminantAnalysis
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html