the two_moons dataset with only hidden unitsthe decision boundary looks somewhat more ragged the default nonlinearity is relushown in figure - with single hidden layerthis means the decision function will be made up of straight line segments if we want smoother decision boundarywe could add more hidden units (as in figure - )add second hidden layer (figure - )or use the tanh nonlinearity (figure - )in[ ]using two hidden layerswith units each mlp mlpclassifier(algorithm=' -bfgs'random_state= hidden_layer_sizes=[ ]mlp fit(x_trainy_trainmglearn plots plot_ d_separator(mlpx_trainfill=truealpha mglearn discrete_scatter(x_train[: ]x_train[: ]y_trainplt xlabel("feature "plt ylabel("feature " supervised learning