Model description

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Intended uses & limitations

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Training Procedure

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Hyperparameters

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Hyperparameter	Value
memory
steps	[('scaler', StandardScaler()), ('svm', SGDClassifier())]
verbose	False
scaler	StandardScaler()
svm	SGDClassifier()
scaler__copy	True
scaler__with_mean	True
scaler__with_std	True
svm__alpha	0.0001
svm__average	False
svm__class_weight
svm__early_stopping	False
svm__epsilon	0.1
svm__eta0	0.0
svm__fit_intercept	True
svm__l1_ratio	0.15
svm__learning_rate	optimal
svm__loss	hinge
svm__max_iter	1000
svm__n_iter_no_change	5
svm__n_jobs
svm__penalty	l2
svm__power_t	0.5
svm__random_state
svm__shuffle	True
svm__tol	0.001
svm__validation_fraction	0.1
svm__verbose	0
svm__warm_start	False

Model Plot

Pipeline(steps=[('scaler', StandardScaler()), ('svm', SGDClassifier())])

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Evaluation Results

Metric	Value
accuracy	0.834443
f1 score	0.707803
precision	0.813203
recall	0.62659

How to Get Started with the Model

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eval_method

The model is evaluated using test split, on accuracy, precision, recall and f1.