CS 422 - machine learning

pair plot

(i,j) shows a scatter plot of variables i and j, and the diagonal entries (i,i) show the marginal density of variable i; all plots are optionally color coded by class label 

decision boundary

in a decision rule that defines the boundary of what something is classified by

decision tree

arrange these nested decision rules into a tree structure

supervised learning

a goal is to automatically come up with classification models (such as a decision tree) so as to reliably predict the labels for any given input

empirical risk

the average loss of the predictor on the training set

epistemic uncertainty / model uncertainty

not be able to perfectly predict the exact output given the input, due to lack of knowledge of the input-output mapping