EASE Material

This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong.

===== NEEMS Lecture: 4. Additional Machine Learning Theory =====

[[https://ease-crc.org/material/ease/machinelearning/decision_trees|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.
{{ :ease:machinelearning:xval.png |}}

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.
{{ :ease:machinelearning:conf_matrix.png |}}

Accuracy, Precision, and Recall are measurements of the quality of a model, just like confusion matrices. If you are more interested, go over to [[https://en.wikipedia.org/wiki/Precision_and_recall|the wiki page]] about Precision and Recall.


In [[https://ease-crc.org/material/ease/machinelearning/classifier_training|the next section]] we will finally train our decision tree model with the techniques we've learned so far.