6.2.9. scikits.learn.linear_model.LogisticRegression¶
- class scikits.learn.linear_model.LogisticRegression(penalty='l2', dual=False, eps=0.0001, C=1.0, fit_intercept=True)¶
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
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
fit predict predict_log_proba predict_proba score - __init__(penalty='l2', dual=False, eps=0.0001, C=1.0, fit_intercept=True)¶
- 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