Calculate a Stated Error-Cost Model
Multiply each error count by its stated weight and add the two contributions.
About 22 minutes, plus any extended practice
Open lesson 1Assess Data-Driven Decisions and Tradeoffs
A model has 3 false positives and 2 false negatives under 2/5 point weights. It detects 6 of 8 positives and receives a 0.6 score under the supplied action policy. Calculate and explain the bounded decision.
Before you begin
- Classify prediction errors and calculate percentages.
Choose by skill rather than age alone. Read with an adult when useful, revisit a lesson, or explore another grade.
Follow the lesson sequence
Compare Recall with the Correct Denominator
Divide detected positives by the number of actual positives in the supplied labeled set.
About 22 minutes, plus any extended practice
Open lesson 2Separate a Prediction from the Decision Policy
Apply the documented action policy rather than treating a score as an automatic real-world verdict.
About 22 minutes, plus any extended practice
Open lesson 3Monitor Drift and Define a Revision Trigger
Compare the measured condition with the stated review rule and identify what further evidence is needed.
About 22 minutes, plus any extended practice
Open lesson 4Review and transfer
After the lessons, try 4 new review cases and a practical task. Each answer has an explanation and a link back to the related lesson.
Open the unit reviewGuest answers are temporary. Keep your own paper or document; this unit does not certify completion or record a school grade.
Topic references
- 2026 CSTA PK–12 Computer Science Standards: topic reference — Algorithms & Design; Ethics & Social Responsibility
These references informed the topic selection. They do not certify complete standards alignment.