COMPSCI C8 Lecture Notes - Lecture 34: Labeled Data, Asteroid Family
14 Mar 2018
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Rows of tables t. row(i) evaluates to ith row of table t t. row(i). item(j) is the value of column j in row i. If all values are numbers, then np. array(t. row(i)) evaluates to an array of all the numbers in the row. To consider each row individually, use for row in t. rows: row. item(cid:894)j(cid:895) . Two attributes x and y: use distance formula. Three attributes x, y, and z: use distance formula again but use z. The accuracy of a classifier on a labeled data set is the proportion of examples that are labeled correctly. Need to compare classifier predictions to true labels. If the labeled data set is sampled at random from a population, then we can infer accuracy on that population. A change in input attributes might change the prediction. Inputs that are very close but result in different predicted labels are on either side of a decision boundary.
