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Task 6 (Supervised Classification) letter Uploaded 13-12-2019 by Evan Peterson
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sklearn.linear_model._stochastic_gradient.SGDClassifier(1)	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...
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_alpha	0.0001
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_average	false
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_class_weight	null
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_early_stopping	false
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_epsilon	0.1
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_eta0	0.0
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_fit_intercept	true
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_l1_ratio	0.15
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_learning_rate	"optimal"
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_loss	"hinge"
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_max_iter	1000
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_n_iter_no_change	5
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_n_jobs	null
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_penalty	"l2"
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_power_t	0.5
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_random_state	36888
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_shuffle	true
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_tol	0.001
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_validation_fraction	0.1
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_verbose	0
sklearn.linear_model._stochastic_gradient.SGDClassifier(1)_warm_start	false