# Hyperparameter Optimization

tuned_parameters = [

    # KNN Classifier(2,3,4)

    {'featureSelection__w': N_GENES,

      'classify': [KNeighborsClassifier()],

      'classify__n_neighbors': [2, 3, 4]

      },

    # Decision Tree Classifier(J48 algorithm)

    {'featureSelection__w': N_GENES,

      'classify': [tree.DecisionTreeClassifier()],

      'classify__criterion':['gini', 'entropy'],

      'classify__min_samples_leaf': [1, 3, 5],

      'classify__max_depth': [3, 6, 9],

      'classify__presort': [True]

      },

    # Neural Network Multi-label Classifier

    {'featureSelection__w': N_GENES,

      'classify': [MLPClassifier()],

      'classify__hidden_layer_sizes': N_LAYERS,

      'classify__activation': ['logistic'],

      'classify__alpha':[0.05, 0.01, 0.005, 0.001],

      'classify__max_iter':[1000],

      'classify__solver': ['lbfgs'],

      'classify__verbose': [True]

      },

    # Naïve Bayes Classifier

    {'featureSelection__w': N_GENES,

      'classify': [naive_bayes.GaussianNB()]

      },

    # AdaBoost Classifier

    {'featureSelection__w': N_GENES,

      'classify': [AdaBoostClassifier()]

      }

]

# Model Selection using Pipeline and Cross validation

kfolds = KFold(n_splits=5, shuffle=True, random_state=myRndSeeds)

model = GridSearchCV(pipe, tuned_parameters, cv=kfolds,

                      return_train_score=True)

model.fit(X, y)

results = pd.DataFrame(model.cv_results_