LogisticRegression #

classsklearn.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='lbfgs',max_iter=100,multi_class='deprecated',verbose=0,warm_start=False,n_jobs=None,l1_ratio=None)[source]#

Logistic Regression (aka logit, MaxEnt) classifier.

This class implements regularized logistic regression using the‘liblinear’ library, ‘newton-cg’, ‘sag’, ‘saga’ and ‘lbfgs’ solvers.Notethat regularization is applied by default. It can handle both denseand sparse input. Use C-ordered arrays or CSR matrices containing 64-bitfloats for optimal performance; any other input format will be converted(and copied).

The ‘newton-cg’, ‘sag’, and ‘lbfgs’ solvers support only L2 regularizationwith primal formulation, or no regularization. The ‘liblinear’ solversupports both L1 and L2 regularization, with a dual formulation only forthe L2 penalty. The Elastic-Net regularization is only supported by the‘saga’ solver.

Formulticlass problems, all solvers but ‘liblinear’ optimize the(penalized) multinomial loss. ‘liblinear’ only handle binary classification but canbe extended to handle multiclass by usingOneVsRestClassifier.

solver	penalty	multinomial multiclass
‘lbfgs’	‘l2’, None	yes
‘liblinear’	‘l1’, ‘l2’	no
‘newton-cg’	‘l2’, None	yes
‘newton-cholesky’	‘l2’, None	yes
‘sag’	‘l2’, None	yes
‘saga’	‘elasticnet’, ‘l1’, ‘l2’, None	yes

See also

SGDClassifier: Incrementally trained logistic regression (when given the parameterloss="log_loss").
LogisticRegressionCV: Logistic regression with built-in cross validation.

Notes

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

Predict output may not match that of standalone liblinear in certaincases. Seedifferences from liblinearin the narrative documentation.

References

L-BFGS-B – Software for Large-scale Bound-constrained Optimization: Ciyou Zhu, Richard Byrd, Jorge Nocedal and Jose Luis Morales.http://users.iems.northwestern.edu/~nocedal/lbfgsb.html
LIBLINEAR – A Library for Large Linear Classification: https://www.csie.ntu.edu.tw/~cjlin/liblinear/
SAG – Mark Schmidt, Nicolas Le Roux, and Francis Bach: Minimizing Finite Sums with the Stochastic Average Gradienthttps://hal.inria.fr/hal-00860051/document
SAGA – Defazio, A., Bach F. & Lacoste-Julien S. (2014).: “SAGA: A Fast Incremental Gradient Method With Supportfor Non-Strongly Convex Composite Objectives”
Hsiang-Fu Yu, Fang-Lan Huang, Chih-Jen Lin (2011). Dual coordinate descent: methods for logistic regression and maximum entropy models.Machine Learning 85(1-2):41-75.https://www.csie.ntu.edu.tw/~cjlin/papers/maxent_dual.pdf

Examples

>>>fromsklearn.datasetsimportload_iris>>>fromsklearn.linear_modelimportLogisticRegression>>>X,y=load_iris(return_X_y=True)>>>clf=LogisticRegression(random_state=0).fit(X,y)>>>clf.predict(X[:2,:])array([0, 0])>>>clf.predict_proba(X[:2,:])array([[9.82e-01, 1.82e-02, 1.44e-08],       [9.72e-01, 2.82e-02, 3.02e-08]])>>>clf.score(X,y)0.97

For a comparison of the LogisticRegression with other classifiers see:Plot classification probability.

decision_function(X)[source]#

Predict confidence scores for samples.

The confidence score for a sample is proportional to the signeddistance of that sample to the hyperplane.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The data matrix for which we want to get the confidence scores.

Returns:

scoresndarray of shape (n_samples,) or (n_samples, n_classes): Confidence scores per(n_samples,n_classes) combination. In thebinary case, confidence score forself.classes_[1] where >0 meansthis class would be predicted.

densify()[source]#

Convert coefficient matrix to dense array format.

Converts thecoef_ member (back) to a numpy.ndarray. This is thedefault format ofcoef_ and is required for fitting, so callingthis method is only required on models that have previously beensparsified; otherwise, it is a no-op.

Returns:

self: Fitted estimator.

fit(X,y,sample_weight=None)[source]#

Fit the model according to the given training data.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Training vector, wheren_samples is the number of samples andn_features is the number of features.
yarray-like of shape (n_samples,): Target vector relative to X.
sample_weightarray-like of shape (n_samples,) default=None: Array of weights that are assigned to individual samples.If not provided, then each sample is given unit weight.
Added in version 0.17:sample_weight support to LogisticRegression.

Returns:

self: Fitted estimator.

Notes

The SAGA solver supports both float64 and float32 bit arrays.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please checkUser Guide on how the routingmechanism works.

Returns:

routingMetadataRequest: AMetadataRequest encapsulatingrouting information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator andcontained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

predict(X)[source]#

Predict class labels for samples in X.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The data matrix for which we want to get the predictions.

Returns:

y_predndarray of shape (n_samples,): Vector containing the class labels for each sample.

predict_log_proba(X)[source]#

Predict logarithm of probability estimates.

The returned estimates for all classes are ordered by thelabel of classes.

Parameters:

Xarray-like of shape (n_samples, n_features): Vector to be scored, wheren_samples is the number of samples andn_features is the number of features.

Returns:

Tarray-like of shape (n_samples, n_classes): Returns the log-probability of the sample for each class in themodel, where classes are ordered as they are inself.classes_.

predict_proba(X)[source]#

Probability estimates.

The returned estimates for all classes are ordered by thelabel 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 ofeach class.Else use a one-vs-rest approach, i.e. calculate the probabilityof each class assuming it to be positive using the logistic functionand normalize these values across all the classes.

Parameters:

Xarray-like of shape (n_samples, n_features): Vector to be scored, wheren_samples is the number of samples andn_features is the number of features.

Returns:

Tarray-like of shape (n_samples, n_classes): Returns the probability of the sample for each class in the model,where classes are ordered as they are inself.classes_.

score(X,y,sample_weight=None)[source]#

Returnaccuracy on provided data and labels.

In multi-label classification, this is the subset accuracywhich is a harsh metric since you require for each sample thateach label set be correctly predicted.

Parameters:

Xarray-like of shape (n_samples, n_features): Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True labels forX.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: Mean accuracy ofself.predict(X) w.r.t.y.

set_fit_request(*,sample_weight:bool|None|str='$UNCHANGED$')→LogisticRegression[source]#

Configure whether metadata should be requested to be passed to thefit method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser Guide on how the routingmechanism works.

The options for each parameter are:

True: metadata is requested, and passed tofit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it tofit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing forsample_weight parameter infit.

Returns:

selfobject: The updated object.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects(such asPipeline). The latter haveparameters of the form<component>__<parameter> so that it’spossible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

set_score_request(*,sample_weight:bool|None|str='$UNCHANGED$')→LogisticRegression[source]#

Configure whether metadata should be requested to be passed to thescore method.

The options for each parameter are:

True: metadata is requested, and passed toscore if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it toscore.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing forsample_weight parameter inscore.

Returns:

selfobject: The updated object.

sparsify()[source]#

Convert coefficient matrix to sparse format.

Converts thecoef_ member to a scipy.sparse matrix, which forL1-regularized models can be much more memory- and storage-efficientthan the usual numpy.ndarray representation.

Theintercept_ member is not converted.

Returns:

self: Fitted estimator.

Notes

For non-sparse models, i.e. when there are not many zeros incoef_,this may actuallyincrease memory usage, so use this method withcare. A rule of thumb is that the number of zero elements, which canbe computed with(coef_==0).sum(), must be more than 50% for thisto provide significant benefits.

After calling this method, further fitting with the partial_fitmethod (if any) will not work until you call densify.