class sklearn.linear_model.LogisticRegression(penalty=’l2’, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver=’warn’, max_iter=100, multi_class=’warn’, verbose=0, warm_start=False, n_jobs=None)
[source]
Logistic Regression (aka logit, MaxEnt) classifier.
In the multiclass case, the training algorithm uses the onevsrest (OvR) scheme if the ‘multi_class’ option is set to ‘ovr’, and uses the cross entropy loss if the ‘multi_class’ option is set to ‘multinomial’. (Currently the ‘multinomial’ option is supported only by the ‘lbfgs’, ‘sag’ and ‘newtoncg’ solvers.)
This class implements regularized logistic regression using the ‘liblinear’ library, ‘newtoncg’, ‘sag’ and ‘lbfgs’ solvers. It can handle both dense and sparse input. Use Cordered arrays or CSR matrices containing 64bit floats for optimal performance; any other input format will be converted (and copied).
The ‘newtoncg’, ‘sag’, and ‘lbfgs’ solvers support only L2 regularization with primal formulation. The ‘liblinear’ solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty.
Read more in the User Guide.
Parameters: 


Attributes: 

See also
SGDClassifier
loss="log"
).LogisticRegressionCV
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.
Predict output may not match that of standalone liblinear in certain cases. See differences from liblinear in the narrative documentation.
>>> from sklearn.datasets import load_iris >>> from sklearn.linear_model import LogisticRegression >>> X, y = load_iris(return_X_y=True) >>> clf = LogisticRegression(random_state=0, solver='lbfgs', ... multi_class='multinomial').fit(X, y) >>> clf.predict(X[:2, :]) array([0, 0]) >>> clf.predict_proba(X[:2, :]) array([[9.8...e01, 1.8...e02, 1.4...e08], [9.7...e01, 2.8...e02, ...e08]]) >>> clf.score(X, y) 0.97...
decision_function (X)  Predict confidence scores for samples. 
densify ()  Convert coefficient matrix to dense array format. 
fit (X, y[, sample_weight])  Fit the model according to the given training data. 
get_params ([deep])  Get parameters for this estimator. 
predict (X)  Predict class labels for samples in X. 
predict_log_proba (X)  Log of probability estimates. 
predict_proba (X)  Probability estimates. 
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. 
sparsify ()  Convert coefficient matrix to sparse format. 
__init__(penalty=’l2’, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver=’warn’, max_iter=100, multi_class=’warn’, verbose=0, warm_start=False, n_jobs=None)
[source]
decision_function(X)
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Predict confidence scores for samples.
The confidence score for a sample is the signed distance of that sample to the hyperplane.
Parameters: 


Returns: 

densify()
[source]
Convert coefficient matrix to dense array format.
Converts the coef_
member (back) to a numpy.ndarray. This is the default format of coef_
and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a noop.
Returns: 


fit(X, y, sample_weight=None)
[source]
Fit the model according to the given training data.
Parameters: 


Returns: 

get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

predict(X)
[source]
Predict class labels for samples in X.
Parameters: 


Returns: 

predict_log_proba(X)
[source]
Log of probability estimates.
The returned estimates for all classes are ordered by the label of classes.
Parameters: 


Returns: 

predict_proba(X)
[source]
Probability estimates.
The returned estimates for all classes are ordered by the label of classes.
For a multi_class problem, if multi_class is set to be “multinomial” the softmax function is used to find the predicted probability of each class. Else use a onevsrest approach, i.e calculate the probability of each class assuming it to be positive using the logistic function. and normalize these values across all the classes.
Parameters: 


Returns: 

score(X, y, sample_weight=None)
[source]
Returns the mean accuracy on the given test data and labels.
In multilabel 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: 


Returns: 

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: 


sparsify()
[source]
Convert coefficient matrix to sparse format.
Converts the coef_
member to a scipy.sparse matrix, which for L1regularized models can be much more memory and storageefficient than the usual numpy.ndarray representation.
The intercept_
member is not converted.
Returns: 


For nonsparse models, i.e. when there are not many zeros in coef_
, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with (coef_ == 0).sum()
, must be more than 50% for this to provide significant benefits.
After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.
sklearn.linear_model.LogisticRegression
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html