Question

Errors have ______ distances from the separator, while correct classifications have _________ distances.

Answer 1

In machine learning, correct classifications have smaller distances from the decision boundary, whereas errors have larger distances, often indicating misclassification.

Step-by-step explanation:

Errors have larger distances from the separator, while correct classifications have smaller distances. This statement is typically related to machine learning and classification problems where a separator, such as a decision boundary in a logistic regression or a hyperplane in a support vector machine, is used to categorize data points. Data points that are correctly classified tend to be closer to the decision boundary on the correct side, whereas misclassified points (errors) are situated further away or on the wrong side of the boundary.