Decision Boundary
Explore how decision boundaries work in logistic regression models by analyzing the linear separation of classes using PyTorch. Understand the role of model weights in defining the boundary, and see how increasing feature dimensions can improve class separability through practical examples and visualizations.
Decision boundary for logistic regression
We have just figured out that whenever z equals zero, we are in the decision boundary. But z is given by a linear combination of features x1 and x2. If we work out some basic operations, we arrive at:
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