sklearn.lda.LDA¶
Warning
DEPRECATED
- class sklearn.lda.LDA(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001)[source]¶
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)[source]¶
- decision_function(X)[source]¶
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)[source]¶
- 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)[source]¶
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)[source]¶
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)[source]¶
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)[source]¶
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)[source]¶
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)[source]¶
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)[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: self :