Thresholds Turn Scores into Decisions

Many classifiers do not begin by producing a hard yes/no answer. They produce a score or probability-like value. A decision threshold turns that score into a category.

For example:

score >= 0.70 -> flag positive
score < 0.70  -> flag negative

Move the threshold and the model parameters may stay exactly the same while the system behavior changes.

A lower threshold usually predicts positive more often. That can increase recall because fewer actual positives are missed, but it can also create more false positives.

A higher threshold usually predicts positive less often. Precision may improve while recall falls.

Suppose one case receives score 0.64.

• At threshold 0.50, it is positive.
• At threshold 0.70, it is negative.

Nothing about the model score changed. The policy translating score into action changed.

That distinction becomes critical in AI systems. Model inference and decision policy are separate parts of the system even when an application hides that separation behind one button.

Choose thresholds from consequences and evidence

The goal is not to maximize every metric at once. That is often impossible.

A defensible threshold decision records:

• the candidate thresholds tested;
• the resulting confusion counts and metrics;
• which error type matters most for the use case;
• why the chosen threshold is acceptable under the stated conditions;
• what should happen when confidence is too low for automatic action.

Before the lab

Predict what will happen to false positives and false negatives as you move a positive threshold from 0.30 toward 0.90. Then test your prediction in the lab.