NEEMS Lecture: 4. Additional Machine Learning Theory
Previously we talked about decision trees. This section explains some of the terms in short. Please follow the lecture for more information on this section.
Cross-validation is a technique of training a model, where training-set and testing-set are interchanged a couple of times, to potentially exclude valleys of falsely learned influence of features.
Confusion Matrices illustrate how well the model labels the data correctly and incorrectly, given any new data. The given table takes the diagnosis of a disease as an example.
Accuracy, Precision, and Recall are measurements of the quality of a model, just like confusion matrices. If you are more interested, go over to the wiki page about Precision and Recall.
In the next section we will finally train our decision tree model with the techniques we've learned so far.