summary2.txt

GaussianNB(priors=None)
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Naive Byes accuracy score = 89.2036124795%
Naive Byes precision score = 89.402455468%
Naive Byes recall score = 89.3483883509%
Naive Byes f1 score = 89.2029556426%
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SVC(C=25, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)
--------------------VALIDATION--------------------------
SVM accuracy score = 97.5349219392%
SVM precision score = 97.5919650192%
SVM recall score = 97.5811637433%
SVM f1 score = 97.5349069598%
--------------------TESTING--------------------------
SVM accuracy score = 96.3054187192%
SVM precision score = 96.3826366559%
SVM recall score = 96.4898595944%
SVM f1 score = 96.3045194628%
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DecisionTreeClassifier(class_weight='balanced', criterion='gini',
            max_depth=20, max_features='log2', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=5, min_weight_fraction_leaf=0.0,
            presort=False, random_state=251254, splitter='best')
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Decision tree accuracy score = 100.0%
Decision tree precision score = 100.0%
Decision tree recall score = 100.0%
Decision tree f1 score = 100.0%
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MLPClassifier(activation='logistic', alpha=0.0001, batch_size='auto',
       beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,
       hidden_layer_sizes=(20, 20, 20), learning_rate='constant',
       learning_rate_init=0.001, max_iter=200, momentum=0.9,
       nesterovs_momentum=True, power_t=0.5, random_state=False,
       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,
       verbose=False, warm_start=False)
--------------------VALIDATION--------------------------
Neural network accuracy score = 100.0%
Neural network precision score = 100.0%
Neural network recall score = 100.0%
Neural network f1 score = 100.0%
--------------------TESTING--------------------------
Neural network accuracy score = 100.0%
Neural network precision score = 100.0%
Neural network recall score = 100.0%
Neural network f1 score = 100.0%
--------------------VALIDATION--------------------------
Voting system accuracy score = 100.0%
Voting system precision score = 100.0%
Voting system recall score = 100.0%
Voting system f1 score = 100.0%
--------------------TESTING--------------------------
Voting system accuracy score = 100.0%
Voting system precision score = 100.0%
Voting system recall score = 100.0%
Voting system f1 score = 100.0%
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Process finished with exit code 0