num_folds = 10
n_jobs = -1
score = 'recall'

pipe_nn = Pipeline([['sc', StandardScaler()],
                    ['NN', MLPClassifier(random_state=rand_state,
                                          alpha(0.05), hidden_layer_sizes(100,),
                                          activation('relu')]])

scores_nn = cross_val_score(estimator=pipe_nn,X=X_train,y=y_train,cv=num_folds,n_jobs=n_jobs, scoring=score)

Training Accuracy: 0.735
Test Accuracy: 0.691
          pred:yes  pred:no
act:yes       129       26
act:no         81      110

             precision    recall  f1-score   support
          0       0.61      0.83      0.71       155
          1       0.81      0.58      0.67       191
avg / total       0.72      0.69      0.69       346

pipe_nn = Pipeline([['sc', StandardScaler()],
                    ['PCA',PCA(random_state=rand_state)],
                    ['NN', MLPClassifier(random_state=rand_state)]])

# Hyperparameters
param_grid = dict(PCA = [None,PCA(2),PCA(4),PCA(6),PCA(9),PCA(10),PCA(12),PCA(15),PCA(18)],
MLP__hidden_layer_sizes = [(100,), (50,100), (50,100,50)],
                   NN__activation = ['tanh', 'relu'],
                   NN__alpha = [0.0001, 0.05])

# Apply grid search
grid_nn = GridSearchCV(estimator=pipe_nn,
                        param_grid=param_grid,
                        cv=num_folds,
                        scoring=score)
gs_nn = grid_nn.fit(X_train, y_train)

Best Parameters: {'PCA': PCA(n_components=18), 'NN__alpha': 0.0001,
                  'NN__hidden_layer_sizes': (10, 50), 'NN__activation': 'relu'}
Training Accuracy: 0.761
Test Accuracy : 0.728

          pred:yes  pred:no
act:yes       117       38
act:no         56      135

             precision    recall  f1-score   support
          0       0.68      0.75      0.71       155
          1       0.78      0.71      0.74       191
avg / total       0.73      0.73      0.73       346