tings of the alpha parameter as you probably have realized by nowthere are many ways to control the complexity of neural networkthe number of hidden layersthe number of units in each hidden layerand the regularization (alphathere are actually even morewhich we won' go into here an important property of neural networks is that their weights are set randomly before learning is startedand this random initialization affects the model that is learned that means that even when using exactly the same parameterswe can obtain very different models when using different random seeds if the networks are largeand their complexity is chosen properlythis should not affect accuracy too muchbut it is worth keeping in mind (particularly for smaller networksfigure - shows plots of several modelsall learned with the same settings of the parametersin[ ]figaxes plt subplots( figsize=( )for iax in enumerate(axes ravel())mlp mlpclassifier(algorithm=' -bfgs'random_state=ihidden_layer_sizes=[ ]mlp fit(x_trainy_trainmglearn plots plot_ d_separator(mlpx_trainfill=truealpha ax=axmglearn discrete_scatter(x_train[: ]x_train[: ]y_trainax=axsupervised machine learning algorithms