Unplugged Activity for Understanding Accuracy vs. Precision in Model Evaluation

📚 Topic Summary

Unplugged activities are a fun way to learn computer science concepts without using a computer! This activity helps you understand the difference between accuracy and precision when we're talking about evaluating models (like in machine learning). Accuracy means how close your model's predictions are to the actual correct answers. Precision, on the other hand, focuses on how consistent your model's predictions are – even if they aren't perfectly accurate. Think of it like throwing darts: accuracy is hitting the bullseye, while precision is getting all your darts close together, even if they're far from the bullseye!

This activity will help you visualize these concepts with a hands-on exercise and then test your knowledge with a fill-in-the-blanks and critical thinking questions!

🧪 Part A: Vocabulary

Instructions: Match the term with its correct definition by writing the corresponding number in the blank space.

1. Term: Accuracy
1. How close the predicted values are to each other.
2. A model consistently predicts the same (incorrect) result.
3. How close the predicted values are to the true values.
4. The process of assessing how well a model performs.
5. A perfect model that has 100% accuracy.
2. Term: Precision
3. Term: Model Evaluation
4. Term: Bias
5. Term: Ideal Model

Answers:

1. 3
2. 1
3. 4
4. 2
5. 5

📝 Part B: Fill in the Blanks

Instructions: Fill in the missing words in the paragraph below.

_________ refers to how close a model's predictions are to the actual values. A model can be _________ without being _________, meaning its predictions are consistently off. _________, on the other hand, refers to the consistency of the predictions, regardless of how close they are to the true values. In model evaluation, we strive for both high __________ and __________ to build reliable models.

Word Bank: (Accuracy, Precise, Accurate, Precision)

Answers:

Accuracy refers to how close a model's predictions are to the actual values. A model can be precise without being accurate, meaning its predictions are consistently off. Precision, on the other hand, refers to the consistency of the predictions, regardless of how close they are to the true values. In model evaluation, we strive for both high accuracy and precision to build reliable models.

🧠 Part C: Critical Thinking

Instructions: Answer the following question in a few sentences.

Why is it important to consider both accuracy and precision when evaluating a model? Give an example of a situation where high precision but low accuracy might be problematic.

Example Answer:

Considering both accuracy and precision is crucial because they provide different insights into a model's performance. A model with high precision but low accuracy could be problematic in a medical diagnosis scenario. For example, a test that is highly precise might consistently identify a specific symptom, but if it fails to accurately detect the presence of a disease, it could lead to missed diagnoses and improper treatment.