Lecture 9 · Logistic regression

Score → probability → decision

Logistic regression keeps a linear score but transforms it into a probability between 0 and 1.

Predict first: If we multiply both w and b by 5, does the decision boundary move? What happens to the predicted probabilities?

1-D logistic model

Decision boundary x*—
P(Y=1 | query)—
Log-odds at query—
Predicted class—

Same boundary, different confidence

Reveal the key idea

The decision boundary is where wx+b=0. Multiplying both by a positive constant leaves that equation unchanged.

But the sigmoid sees a larger-magnitude logit away from the boundary, so probabilities become more extreme. The same 0/1 predictions can therefore have very different confidence.