8.14.2.5. sklearn.linear_model.sparse.LogisticRegression

class sklearn.linear_model.sparse.LogisticRegression(penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, scale_C=False)

Logistic Regression.

Implements L1 and L2 regularized logistic regression.

Parameters :

penalty : string, ‘l1’ or ‘l2’

Used to specify the norm used in the penalization

dual : boolean

Dual or primal formulation. Dual formulation is only implemented for l2 penalty.

C : float

Specifies the strength of the regularization. The smaller it is the bigger in the regularization.

fit_intercept : bool, default: True

Specifies if a constant (a.k.a. bias or intercept) should be added the decision function

intercept_scaling : float, default: 1

when self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a “synthetic” feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased

tol: float, optional :

tolerance for stopping criteria

scale_C : bool

Scale C with number of samples. It makes the setting of C independant of the number of samples.

See also

LinearSVC

Notes

The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon, to have slightly different results for the same input data. If that happens, try with a smaller tol parameter.

References: LIBLINEAR – A Library for Large Linear Classification http://www.csie.ntu.edu.tw/~cjlin/liblinear/

Attributes

coef_	array, shape = [n_classes-1, n_features]	Coefficient of the features in the decision function.
intercept_	array, shape = [n_classes-1]	intercept (a.k.a. bias) added to the decision function. It is available only when parameter intercept is set to True

Methods

decision_function(X)	Return the decision function of X according to the trained
fit(X, y[, class_weight])	Fit the model using X, y as training data.
fit_transform(X[, y])	Fit to data, then transform it
predict(X)	Predict target values of X according to the fitted model.
predict_log_proba(T)	Log of Probability estimates.
predict_proba(X)	Probability estimates.
score(X, y)	Returns the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of the estimator.
transform(X[, threshold])

__init__(penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, scale_C=False)

decision_function(X)

Return the decision function of X according to the trained model.

Parameters :

X : sparse matrix, shape = [n_samples, n_features]

Returns :

T : array-like, shape = [n_samples, n_class]

Returns the decision function of the sample for each class in the model.

fit(X, y, class_weight=None)

Fit the model using X, y as training data.

Parameters :

X : sparse matrix, shape = [n_samples, n_features]

Training vector, where n_samples in the number of samples and n_features is the number of features.

y : array, shape = [n_samples]

Target vector relative to X

Returns :

self : object

Returns an instance of self.

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.

Notes

This method just calls fit and transform consecutively, i.e., it is not an optimized implementation of fit_transform, unlike other transformers such as PCA.

predict(X)

Predict target values of X according to the fitted model.

Parameters :X : sparse matrix, shape = [n_samples, n_features] Returns :C : array, shape = [n_samples]

predict_log_proba(T)

Log of Probability estimates.

The returned estimates for all classes are ordered by the label of classes.

predict_proba(X)

Probability estimates.

The returned estimates for all classes are ordered by the label of classes.

score(X, y)

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

Parameters :

X : array-like, shape = [n_samples, n_features]

Training set.

y : array-like, shape = [n_samples]

Labels for X.

Returns :

z : float

set_params(**params)

Set the parameters of the estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :