One-vs-Rest (OvR) Heuristic Method

The video explains the one-vs-rest method for multiclass classification using binary classifiers. It uses a fruit dataset with four classes: apples, bananas, oranges, and watermelons. Each class is turned into a binary classification problem, where one class is positive and the others are negative. Logistic regression models are trained for each class, and predictions are made based on the highest confidence value from these models. The video concludes with a brief mention of the next topic, one-vs-one classification.

What is the significance of the confidence value in the classification process?

How does the model determine the final prediction when given a new input?