This chapter walked us through a practical implementation of machine learning in Ruby. It also showed a number of ways in which we can build machine learning applications.
We looked at how to leverage the decision tree library in order to train a program via historical data and then let the program make an informed recommendation. We analyzed how to extend our decision tree knowledge to build a more complex machine learning algorithm.
We reviewed how to leverage machine learning in order to build out a complex recommendation engine. This type of tool can be utilized in real-world applications to help an organization make informed decisions based on historical data.
