sklearn.lda.LDA

Warning

DEPRECATED

class sklearn.lda.LDA(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001)

Alias for sklearn.discriminant_analysis.LinearDiscriminantAnalysis.

Deprecated since version 0.17: This class will be removed in 0.19. Use sklearn.discriminant_analysis.LinearDiscriminantAnalysis instead.

Methods

decision_function(X)	Predict confidence scores for samples.
fit(X, y[, store_covariance, tol])	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)

decision_function(X)

Predict confidence scores for samples.

The confidence score for a sample is the signed distance of that sample to the hyperplane.

Parameters:

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

Samples.

Returns:

array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes) :

Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.

fit(X, y, store_covariance=None, tol=None)

Fit LinearDiscriminantAnalysis model according to the given

training data and parameters.

Changed in version 0.17: Deprecated store_covariance have been moved to main constructor.

Changed in version 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.

fit_transform(X, y=None, **fit_params)

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:

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

Returns:

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

predict(X)

Predict class labels for samples in X.

Parameters:

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Samples.

Returns:

C : array, shape = [n_samples]

Predicted class label per sample.

predict_log_proba(X)

Estimate log probability.

Parameters:

X : array-like, shape (n_samples, n_features)

Input data.

Returns:

C : array, shape (n_samples, n_classes)

Estimated log probabilities.

predict_proba(X)

Estimate probability.

Parameters:

X : array-like, shape (n_samples, n_features)

Input data.

Returns:

C : array, shape (n_samples, n_classes)

Estimated probabilities.

score(X, y, sample_weight=None)

Returns the mean accuracy on the given test data and labels.

In multi-label 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:

X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.

Returns:

score : float

Mean accuracy of self.predict(X) wrt. y.

set_params(**params)

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:self :

transform(X)

Project data to maximize class separation.

Parameters:

X : array-like, shape (n_samples, n_features)

Input data.

Returns:

X_new : array, shape (n_samples, n_components)

Transformed data.