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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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alpha | Constant that multiplies the regularization term. Defaults to 0.0001 Also used to compute learning_rate when set to 'optimal' | default: 0.0001 |

average | When 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_weight | Preset 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_stopping | Whether 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:: 0.20 | default: false |

epsilon | Epsilon 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 threshold | default: 0.1 |

eta0 | The 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_intercept | Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True | default: true |

l1_ratio | The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1 l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1 Defaults to 0.15 | default: 0.15 |

learning_rate | The 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 5 | default: "optimal" |

loss | The 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 description | default: "hinge" |

max_iter | The 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:: 0.19 | default: 1000 |

n_iter_no_change | Number of iterations with no improvement to wait before early stopping .. versionadded:: 0.20 | default: 5 |

n_jobs | The 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 | default: null |

penalty | The 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_t | The exponent for inverse scaling learning rate [default 0.5] | default: 0.5 |

random_state | 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` | default: null |

shuffle | Whether or not the training data should be shuffled after each epoch Defaults to True | default: true |

tol | The stopping criterion. If it is not None, the iterations will stop when (loss > best_loss - tol) for ``n_iter_no_change`` consecutive epochs .. versionadded:: 0.19 | default: 0.001 |

validation_fraction | The 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:: 0.20 | default: 0.1 |

verbose | The verbosity level | default: 0 |

warm_start | When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution
See :term:`the Glossary | default: false |

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