text mining and text analytics takes into account when constructing the branches when multiple terms together create stronger classification than single termsthe decision tree will actually outperform the naive bayes we won' go into the details of that herebut consider this one of the next steps you could take to improve the model we now have two classification models that give us insight into how the two contents of the subreddits differ the last step would be to share this newfound information with other people step presentation and automation as last step we need to use what we learned and either turn it into useful application or present our results to others the last of this book discusses building an interactive applicationas this is project in itself for now we'll content ourselves with nice way to convey our findings nice graph orbetter yetan interactive graphcan catch the eyeit' the icing on the presentation cake while it' easy and tempting to represent the numbers as such or bar chart at mostit could be nice to go one step further for instanceto represent the naive bayes modelwe could use force graph (figure )where the bubble and link size represent how strongly related word is to the "game of thronesor "data sciencesubreddits notice how the words on the bubbles are often cut offremember this is because of the stemming we applied figure interactive force graph with the top naive bayes significant terms and their weights while figure in itself is staticyou can open the html file "forcegraph htmlto enjoy the js force graph effect as explained earlier in this js is outside of this book' scope but you don' need an elaborate knowledge of js to use it an extensive set of examples can be used with minimal adjustments to the code provided at