val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?penalty:[ `L1 | `L2 | `Elasticnet | `None ] ->
?dual:bool ->
?tol:float ->
?c:float ->
?fit_intercept:bool ->
?intercept_scaling:float ->
?class_weight:[ `DictIntToFloat of (int * float) list | `Balanced ] ->
?random_state:int ->
?solver:[ `Newton_cg | `Lbfgs | `Liblinear | `Sag | `Saga ] ->
?max_iter:int ->
?multi_class:[ `Auto | `Ovr | `Multinomial ] ->
?verbose:int ->
?warm_start:bool ->
?n_jobs:int ->
?l1_ratio:float ->
unit ->
tLogistic Regression (aka logit, MaxEnt) classifier.
In the multiclass case, the training algorithm uses the one-vs-rest (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', 'saga' and 'newton-cg' solvers.)
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.
Read more in the :ref:`User Guide <logistic_regression>`.
Parameters ---------- penalty : 'l1', 'l2', 'elasticnet', 'none', default='l2' Used to specify the norm used in the penalization. The 'newton-cg', 'sag' and 'lbfgs' solvers support only l2 penalties. 'elasticnet' is only supported by the 'saga' solver. If 'none' (not supported by the liblinear solver), no regularization is applied.
.. versionadded:: 0.19 l1 penalty with SAGA solver (allowing 'multinomial' + L1)
dual : bool, default=False Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features.
tol : float, default=1e-4 Tolerance for stopping criteria.
C : float, default=1.0 Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization.
fit_intercept : bool, default=True Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.
intercept_scaling : float, default=1 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/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.
class_weight : dict or 'balanced', default=None 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.
.. versionadded:: 0.17 *class_weight='balanced'*
random_state : int, RandomState instance, default=None The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Used when ``solver`` == 'sag' or 'liblinear'.
solver : 'newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga', default='lbfgs'
Algorithm to use in the optimization problem.
Note that '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.
.. versionadded:: 0.17 Stochastic Average Gradient descent solver. .. versionadded:: 0.19 SAGA solver. .. versionchanged:: 0.22 The default solver changed from 'liblinear' to 'lbfgs' in 0.22.
max_iter : int, default=100 Maximum number of iterations taken for the solvers to converge.
multi_class : 'auto', 'ovr', 'multinomial', default='auto' 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'.
.. versionadded:: 0.18 Stochastic Average Gradient descent solver for 'multinomial' case. .. versionchanged:: 0.22 Default changed from 'ovr' to 'auto' in 0.22.
verbose : int, default=0 For the liblinear and lbfgs solvers set verbose to any positive number for verbosity.
warm_start : bool, default=False 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 :term:`the Glossary <warm_start>`.
.. versionadded:: 0.17 *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.
n_jobs : int, default=None 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 :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.
l1_ratio : float, default=None 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.
Attributes ----------
classes_ : ndarray of shape (n_classes, ) A list of class labels known to the classifier.
coef_ : ndarray of shape (1, n_features) or (n_classes, n_features) 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_ : ndarray of shape (1,) or (n_classes,) 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).
n_iter_ : ndarray of shape (n_classes,) or (1, ) 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.
.. versionchanged:: 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"``). 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 :ref:`differences from liblinear <liblinear_differences>` in 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 Gradient https://hal.inria.fr/hal-00860051/document
SAGA -- Defazio, A., Bach F. & Lacoste-Julien S. (2014). SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives https://arxiv.org/abs/1407.0202
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 -------- >>> 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([9.8...e-01, 1.8...e-02, 1.4...e-08],
[9.7...e-01, 2.8...e-02, ...e-08]) >>> clf.score(X, y) 0.97..."
Predict confidence scores for samples.
The confidence score for a sample is the signed distance of that sample to the hyperplane.
Parameters ---------- X : array_like or sparse matrix, shape (n_samples, n_features) Samples.
Returns ------- array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes) Confidence scores per (sample, class) 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.
Returns ------- self Fitted estimator.
Fit the model according to the given training data.
Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_features is the number of features.
y : array-like of shape (n_samples,) Target vector relative to X.
sample_weight : array-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.
.. versionadded:: 0.17 *sample_weight* support to LogisticRegression.
Returns ------- self Fitted estimator.
Notes ----- The SAGA solver supports both float64 and float32 bit arrays.
Get parameters for this estimator.
Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : mapping of string to any Parameter names mapped to their values.
Predict class labels for samples in X.
Parameters ---------- X : array_like or sparse matrix, shape (n_samples, n_features) Samples.
Returns ------- C : array, shape n_samples Predicted class label per sample.
Predict logarithm of probability estimates.
The returned estimates for all classes are ordered by the label of classes.
Parameters ---------- X : array-like of shape (n_samples, n_features) Vector to be scored, where `n_samples` is the number of samples and `n_features` is the number of features.
Returns ------- T : array-like of shape (n_samples, n_classes) 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.
Parameters ---------- X : array-like of shape (n_samples, n_features) Vector to be scored, where `n_samples` is the number of samples and `n_features` is the number of features.
Returns ------- T : array-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 in ``self.classes_``.
Return the mean accuracy on the given test 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.
Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float Mean accuracy of self.predict(X) wrt. y.
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet 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.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
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.
Returns ------- self 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.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.