6.2.10. scikits.learn.linear_model.LogisticRegression

class scikits.learn.linear_model.LogisticRegression(penalty='l2', dual=False, eps=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1)

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

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 eps 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 according to the given training data and parameters.
predict(X)	Predict target values of X according to the fitted model.
predict_log_proba(X)	Log of Probability estimates.
predict_proba(X)	Probability estimates.
score(X, y)	Returns the mean error rate on the given test data and labels.
transform(X[, threshold])

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

decision_function(X)

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

Parameters :

X : array-like, 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={}, **params)

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

Parameters :

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

Target vector relative to X

class_weight : dict , {class_label

Weights associated with classes. If not given, all classes are supposed to have weight one.

Returns :

self : object

Returns self.

predict(X)

Predict target values of X according to the fitted model.

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

predict_log_proba(X)

Log of Probability estimates.

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

Parameters :

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

Returns :

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

Returns the log-probabilities of the sample for each class in the model, where classes are ordered by arithmetical order.

predict_proba(X)

Probability estimates.

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

Parameters :

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

Returns :

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

Returns the probability of the sample for each class in the model, where classes are ordered by arithmetical order.

score(X, y)

Returns the mean error rate 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