Deep Learning - Deep Neural Network for Beginners Using Python - Weighted Sums

The video tutorial explains the concept of weighted sums to prioritize model M1 over M2 based on performance. It demonstrates assigning weights to models, calculating weighted probabilities, and using the sigmoid function to determine the probability of a point belonging to a class. The tutorial emphasizes the importance of assigning higher weights to better-performing models while maintaining the contribution of less effective models to achieve a nonlinear boundary.

What does the teacher mean by 'assigning heavyweights' to certain models?

Why is it important to keep model M2 even if its impact is reduced?