we can visualize the decision tree with the help of the following python codedot_data tree export_graphviz(clf,feature_names=data_feature_names,out_file=none,filledtrue,rounded=truegraph pydotplus graph_from_dot_data(dot_datacolors ('orange''yellow'edges collections defaultdict(listfor edge in graph get_edge_list():edges[edge get_source()append(int(edge get_destination))for edge in edgesedges[edgesort(for in range( ):dest graph get_node(str(edges[edge][ ]))[ dest set_fillcolor(colors[ ]graph write_png('decisiontree png'it will give the prediction for the above code as ['woman'and create the following decision tree