Recap Terms#

Recap#

Last time we learned a lot about the basic terminology and ideas from Machine Learning! We learned the following bolded terms. There were some terms that appear below that we didn’t bold last time since they didn’t appear in the first part of the reading. They are still useful to know.

  • Machine learning

  • Training Set

  • Example

  • Feature

  • Label

  • Machine learning algorithm (or simply “learning algorithm”)

  • Model

  • Regression

  • Classification

  • Decision Tree: A model that works as a series of splits to reach decisions at the leaf nodes.

    • Do note that Decision Trees are only one type of model! There are hundreds if not thousands of different types of models that represent their rules differently!

  • Accuracy: Specifically, classification accuracy or the fraction of examples predicted correctly.

  • Mean Square Error: A measure of how much error was made by a regression model.

Make sure you have these terms in your notes since will assume you are familiar with them!