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sklearn.linear_model._stochastic_gradient.SGDClassifier

sklearn.linear_model._stochastic_gradient.SGDClassifier

Visibility: public Uploaded 13-12-2019 by Evan Peterson sklearn==0.22 numpy>=1.6.1 scipy>=0.9 0 runs
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  • openml-python python scikit-learn sklearn sklearn_0.22
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Linear classifiers (SVM, logistic regression, a.o.) with SGD training. This estimator implements regularized linear models with stochastic gradient descent (SGD) learning: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate). SGD allows minibatch (online/out-of-core) learning, see the partial_fit method. For best results using the default learning rate schedule, the data should have zero mean and unit variance. This implementation works with data represented as dense or sparse arrays of floating point values for the features. The model it fits can be controlled with the loss parameter; by default, it fits a linear support vector machine (SVM). The regularizer is a penalty added to the loss function that shrinks model parameters towards the zero vector using either the squared euclidean norm L2 or the absolute norm L1 or a combination of both (Elastic Net). If the parameter update crosses the 0.0 value b...

Parameters

alphaConstant that multiplies the regularization term. Defaults to 0.0001 Also used to compute learning_rate when set to 'optimal'default: 0.0001
averageWhen set to True, computes the averaged SGD weights and stores the result in the ``coef_`` attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So ``average=10`` will begin averaging after seeing 10 samples.default: false
class_weightPreset for the class_weight fit parameter Weights associated with classes. 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))``default: null
early_stoppingWhether to use early stopping to terminate training when validation score is not improving. If set to True, it will automatically set aside a stratified fraction of training data as validation and terminate training when validation score is not improving by at least tol for n_iter_no_change consecutive epochs .. versionadded:: 0.20default: false
epsilonEpsilon in the epsilon-insensitive loss functions; only if `loss` is 'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive' For 'huber', determines the threshold at which it becomes less important to get the prediction exactly right For epsilon-insensitive, any differences between the current prediction and the correct label are ignored if they are less than this thresholddefault: 0.1
eta0The initial learning rate for the 'constant', 'invscaling' or 'adaptive' schedules. The default value is 0.0 as eta0 is not used by the default schedule 'optimal'default: 0.0
fit_interceptWhether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to Truedefault: true
l1_ratioThe Elastic Net mixing parameter, with 0 <= l1_ratio <= 1 l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1 Defaults to 0.15default: 0.15
learning_rateThe learning rate schedule: 'constant': eta = eta0 'optimal': [default] eta = 1.0 / (alpha * (t + t0)) where t0 is chosen by a heuristic proposed by Leon Bottou 'invscaling': eta = eta0 / pow(t, power_t) 'adaptive': eta = eta0, as long as the training keeps decreasing Each time n_iter_no_change consecutive epochs fail to decrease the training loss by tol or fail to increase validation score by tol if early_stopping is True, the current learning rate is divided by 5default: "optimal"
lossThe loss function to be used. Defaults to 'hinge', which gives a linear SVM The possible options are 'hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron', or a regression loss: 'squared_loss', 'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive' The 'log' loss gives logistic regression, a probabilistic classifier 'modified_huber' is another smooth loss that brings tolerance to outliers as well as probability estimates 'squared_hinge' is like hinge but is quadratically penalized 'perceptron' is the linear loss used by the perceptron algorithm The other losses are designed for regression but can be useful in classification as well; see SGDRegressor for a descriptiondefault: "hinge"
max_iterThe maximum number of passes over the training data (aka epochs) It only impacts the behavior in the ``fit`` method, and not the :meth:`partial_fit` method .. versionadded:: 0.19default: 1000
n_iter_no_changeNumber of iterations with no improvement to wait before early stopping .. versionadded:: 0.20default: 5
n_jobsThe number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context ``-1`` means using all processors. See :term:`Glossary ` for more detailsdefault: null
penaltyThe penalty (aka regularization term) to be used. Defaults to 'l2' which is the standard regularizer for linear SVM models. 'l1' and 'elasticnet' might bring sparsity to the model (feature selection) not achievable with 'l2'default: "l2"
power_tThe exponent for inverse scaling learning rate [default 0.5]default: 0.5
random_stateThe 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`default: null
shuffleWhether or not the training data should be shuffled after each epoch Defaults to Truedefault: true
tolThe stopping criterion. If it is not None, the iterations will stop when (loss > best_loss - tol) for ``n_iter_no_change`` consecutive epochs .. versionadded:: 0.19default: 0.001
validation_fractionThe proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1 Only used if early_stopping is True .. versionadded:: 0.20default: 0.1
verboseThe verbosity leveldefault: 0
warm_startWhen set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution See :term:`the Glossary ` Repeatedly calling fit or partial_fit when warm_start is True can result in a different solution than when calling fit a single time because of the way the data is shuffled If a dynamic learning rate is used, the learning rate is adapted depending on the number of samples already seen. Calling ``fit`` resets this counter, while ``partial_fit`` will result in increasing the existing counterdefault: false

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