'\u03BB', 'parameters', col_names_nn_Keras_regressor, row_names_nn_Keras_regressor, True, savefig=True, figname='Images/NN_reg_accuracy_' + wine_type + '.png') #refit best NN regressor print(clf.best_params_) nnKerasRegBest = KerasRegressor(build_fn=build_network, n_outputs=1, output_activation=None, loss="mean_squared_error", verbose=0) nnKerasRegBest.set_params(**clf.best_params_) hist = nnKerasRegBest.fit(Xtrain, ytrain, validation_data=(Xtest, ytest)) pred_nnKerasRegBest_train = nnKerasRegBest.predict(Xtrain) pred_nnKerasRegBest_test = nnKerasRegBest.predict(Xtest) print('Neural network regressor MSE train: %g' % mean_squared_error(ytrain, pred_nnKerasRegBest_train)) print('Neural network regressor MSE test: %g' % mean_squared_error(ytest, pred_nnKerasRegBest_test)) print('Neural network regressor MAD train: %g' % MAD(ytrain, pred_nnKerasRegBest_train)) print('Neural network regressor MAD test: %g' % MAD(ytest, pred_nnKerasRegBest_test)) print('Neural network regressor accuracy train: %g' % accuracy_score(ytrain, np.rint(pred_nnKerasRegBest_train))) print('Neural network regressor accuracy test: %g' % accuracy_score(ytest, np.rint(pred_nnKerasRegBest_test)))