Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Making Predictions (Classes)
classes = (predictions >= 0).astype(np.int)
classes
Output
array([[1],
[1],
[1],
[0]])
Clearly, the points where the logits (z) equal zero determine the boundary
between positive and negative examples.
"Why 0.5? Can I choose a different threshold?"
Sure, you can! Different thresholds will give you different confusion matrices and,
therefore, different metrics, like accuracy, precision, and recall. We’ll get back to
that in the "Decision Boundary" section.
By the way, are you still holding that thought about the "glorified linear regression?"
Good!
Decision Boundary
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 x 1 and x 2 . If we work
out some basic operations, we arrive at:
Equation 3.22 - Decision boundary for logistic regression with two features
Given our model (b, w 1 , and w 2 ), for any value of the first feature (x 1 ), we can
compute the corresponding value of the second feature (x 2 ) that sits exactly at the
236 | Chapter 3: A Simple Classification Problem