Reviewing the outputs printed after each model build, we should notice that the decision tree model has one of the best results:
Accuracy of Decision Tree classifier on training set: 0.99
Accuracy of Decision Tree classifier on test set: 0.00
Evaluating the classifier
A disclaimer of sorts
Typically, we would spend much more time evaluating and verifying the performance of a selected model (and continually training it), but again, you get the general idea (there is plenty of due diligence work to do!), and our goals are more around demonstrating the steps in building an end-to-end machine learning solution using IBM Watson Studio and its resources.
With that in mind, we will now use some Python code to create some visualizations...
