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', MinMaxScaler()), ('mlp', MLPClassifier(verbose=True))]
verbose	False
scaler	MinMaxScaler()
mlp	MLPClassifier(verbose=True)
scaler__clip	False
scaler__copy	True
scaler__feature_range	(0, 1)
mlp__activation	relu
mlp__alpha	0.0001
mlp__batch_size	auto
mlp__beta_1	0.9
mlp__beta_2	0.999
mlp__early_stopping	False
mlp__epsilon	1e-08
mlp__hidden_layer_sizes	(100,)
mlp__learning_rate	constant
mlp__learning_rate_init	0.001
mlp__max_fun	15000
mlp__max_iter	200
mlp__momentum	0.9
mlp__n_iter_no_change	10
mlp__nesterovs_momentum	True
mlp__power_t	0.5
mlp__random_state
mlp__shuffle	True
mlp__solver	adam
mlp__tol	0.0001
mlp__validation_fraction	0.1
mlp__verbose	True
mlp__warm_start	False

Model Plot

Pipeline(steps=[('scaler', MinMaxScaler()),('mlp', MLPClassifier(verbose=True))])

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Evaluation Results

Metric	Value
accuracy	0.848003
f1 score	0.756372
precision	0.779951
recall	0.734177

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.