Logistic Regression (aka logit, MaxEnt) classifier.
This class implements regularized logistic regression using the ‘liblinear’ library, ‘newton-cg’, ‘sag’, ‘saga’ and ‘lbfgs’ solvers. Note that regularization is applied by default. It can handle both dense and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit floats for optimal performance; any other input format will be converted (and copied).
The ‘newton-cg’, ‘sag’, and ‘lbfgs’ solvers support only L2 regularization with primal formulation, or no regularization. The ‘liblinear’ solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty. The Elastic-Net regularization is only supported by the ‘saga’ solver.
For multiclass problems, all solvers but ‘liblinear’ optimize the (penalized) multinomial loss. ‘liblinear’ only handle binary classification but can be extended to handle multiclass by using OneVsRestClassifier.
Read more in the User Guide.
Specify the norm of the penalty:
None: no penalty is added;
'l2': add a L2 penalty term and it is the default choice;
'l1': add a L1 penalty term;
'elasticnet': both L1 and L2 penalty terms are added.
Warning
Some penalties may not work with some solvers. See the parameter solver below, to know the compatibility between the penalty and solver.
Added in version 0.19: l1 penalty with SAGA solver (allowing ‘multinomial’ + L1)
Dual (constrained) or primal (regularized, see also this equation) formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features.
Tolerance for stopping criteria.
Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization.
Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.
Useful only when the solver liblinear is used and self.fit_intercept is set to True. In this case, x becomes [x, self.intercept_scaling], i.e. a “synthetic” feature with constant value equal 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 or 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.
Weights associated with classes in the form {class_label: weight}. If not given, all classes are supposed to have weight one.
The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).
Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.
Added in version 0.17: class_weight=’balanced’
Used when solver == ‘sag’, ‘saga’ or ‘liblinear’ to shuffle the data. See Glossary for details.
Algorithm to use in the optimization problem. Default is ‘lbfgs’. To choose a solver, you might want to consider the following aspects:
For small datasets, ‘liblinear’ is a good choice, whereas ‘sag’ and ‘saga’ are faster for large ones;
For multiclass problems, all solvers except ‘liblinear’ minimize the full multinomial loss;
‘liblinear’ can only handle binary classification by default. To apply a one-versus-rest scheme for the multiclass setting one can wrap it with the OneVsRestClassifier.
‘newton-cholesky’ is a good choice for n_samples >> n_features * n_classes, especially with one-hot encoded categorical features with rare categories. Be aware that the memory usage of this solver has a quadratic dependency on n_features * n_classes because it explicitly computes the full Hessian matrix.
Warning
The choice of the algorithm depends on the penalty chosen and on (multinomial) multiclass support:
Note
‘sag’ and ‘saga’ fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing.
Added in version 0.17: Stochastic Average Gradient (SAG) descent solver. Multinomial support in version 0.18.
Added in version 0.19: SAGA solver.
Changed in version 0.22: The default solver changed from ‘liblinear’ to ‘lbfgs’ in 0.22.
Added in version 1.2: newton-cholesky solver. Multinomial support in version 1.6.
Maximum number of iterations taken for the solvers to converge.
If the option chosen is ‘ovr’, then a binary problem is fit for each label. For ‘multinomial’ the loss minimised is the multinomial loss fit across the entire probability distribution, even when the data is binary. ‘multinomial’ is unavailable when solver=’liblinear’. ‘auto’ selects ‘ovr’ if the data is binary, or if solver=’liblinear’, and otherwise selects ‘multinomial’.
Added in version 0.18: Stochastic Average Gradient descent solver for ‘multinomial’ case.
Changed in version 0.22: Default changed from ‘ovr’ to ‘auto’ in 0.22.
Deprecated since version 1.5: multi_class was deprecated in version 1.5 and will be removed in 1.7. From then on, the recommended ‘multinomial’ will always be used for n_classes >= 3. Solvers that do not support ‘multinomial’ will raise an error. Use sklearn.multiclass.OneVsRestClassifier(LogisticRegression()) if you still want to use OvR.
For the liblinear and lbfgs solvers set verbose to any positive number for verbosity.
When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. Useless for liblinear solver. See the Glossary.
Added in version 0.17: warm_start to support lbfgs, newton-cg, sag, saga solvers.
Number of CPU cores used when parallelizing over classes if multi_class=’ovr’”. This parameter is ignored when the solver is set to ‘liblinear’ regardless of whether ‘multi_class’ is specified or not. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
The Elastic-Net mixing parameter, with 0 <= l1_ratio <= 1. Only used if penalty='elasticnet'. Setting l1_ratio=0 is equivalent to using penalty='l2', while setting l1_ratio=1 is equivalent to using penalty='l1'. For 0 < l1_ratio <1, the penalty is a combination of L1 and L2.
A list of class labels known to the classifier.
Coefficient of the features in the decision function.
coef_ is of shape (1, n_features) when the given problem is binary. In particular, when multi_class='multinomial', coef_ corresponds to outcome 1 (True) and -coef_ corresponds to outcome 0 (False).
Intercept (a.k.a. bias) added to the decision function.
If fit_intercept is set to False, the intercept is set to zero. intercept_ is of shape (1,) when the given problem is binary. In particular, when multi_class='multinomial', intercept_ corresponds to outcome 1 (True) and -intercept_ corresponds to outcome 0 (False).
Number of features seen during fit.
Added in version 0.24.
n_features_in_,)
Names of features seen during fit. Defined only when X has feature names that are all strings.
Added in version 1.0.
Actual number of iterations for all classes. If binary or multinomial, it returns only 1 element. For liblinear solver, only the maximum number of iteration across all classes is given.
Changed in version 0.20: In SciPy <= 1.0.0 the number of lbfgs iterations may exceed max_iter. n_iter_ will now report at most max_iter.
See also
SGDClassifier
Incrementally trained logistic regression (when given the parameter loss="log_loss").
LogisticRegressionCV
Logistic regression with built-in cross validation.
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.
Predict output may not match that of standalone liblinear in certain cases. See differences from liblinear in the narrative documentation.
References
Ciyou Zhu, Richard Byrd, Jorge Nocedal and Jose Luis Morales. http://users.iems.northwestern.edu/~nocedal/lbfgsb.html
Minimizing Finite Sums with the Stochastic Average Gradient https://hal.inria.fr/hal-00860051/document
“SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives”
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
>>> 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).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.
Predict confidence scores for samples.
The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.
The data matrix for which we want to get the confidence scores.
Confidence scores per (n_samples, n_classes) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.
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 no-op.
Fitted estimator.
Fit the model according to the given training data.
Training vector, where n_samples is the number of samples and n_features is the number of features.
Target vector relative to X.
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.
Fitted estimator.
Notes
The SAGA solver supports both float64 and float32 bit arrays.
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
A MetadataRequest encapsulating routing information.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Predict class labels for samples in X.
The data matrix for which we want to get the predictions.
Vector containing the class labels for each sample.
Predict logarithm of probability estimates.
The returned estimates for all classes are ordered by the label of classes.
Vector to be scored, where n_samples is the number of samples and n_features is the number of features.
Returns the log-probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.
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 one-vs-rest 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.
Vector to be scored, where n_samples is the number of samples and n_features is the number of features.
Returns the probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.
Return accuracy on provided data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Test samples.
True labels for X.
Sample weights.
Mean accuracy of self.predict(X) w.r.t. y.
Configure whether metadata should be requested to be passed to the fit method.
Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True(seesklearn.set_config). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True: metadata is requested, and passed tofitif 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 the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
Metadata routing for sample_weight parameter in fit.
The updated object.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Estimator parameters.
Estimator instance.
Configure whether metadata should be requested to be passed to the score method.
Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True(seesklearn.set_config). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True: metadata is requested, and passed toscoreif 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 the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
Metadata routing for sample_weight parameter in score.
The updated object.
Convert coefficient matrix to sparse format.
Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.
The intercept_ member is not converted.
Fitted estimator.
Notes
For non-sparse 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.
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