DATA1002 Chap.9 Predictive Models, Evaluation and Explainability

Predictive Models, Evaluation and Explainability

Define predictive model

The course material gives this chapter a concrete anchor: Weeks 10-11 and Project Stage 2 require a predictive model, evaluation of success and explanation through a group poster.

That predictive model anchor controls how evaluation metric is explained and how generalisation is tested in changed practice.

Predictive Models, Evaluation and Explainability turns predictive model, evaluation metric and generalisation into executable reasoning.

The chapter's practical target is to fit and evaluate a predictive model using held-out evidence and a metric aligned to the decision, so every explanation should connect syntax to program state, control flow and observable output.

Treat predictive model as a precise program object, not a loose label.

Identify the value or responsibility of predictive model before execution, then trace what can read it, change it or depend on it.

This makes state changes visible before they become debugging guesses.

Trace evaluation metric

Use evaluation metric to explain the program's next move.

Work through one representative evaluation metric input by hand and name the branch, iteration or call that follows. If the evaluation metric trace cannot be stated, the code may run by accident rather than by understood design.

Bring in generalisation as the test of structure.

Compare normal, boundary and invalid inputs for generalisation; state the expected behaviour first; then use the mismatch between expectation and result to localise the defect.

For the application — fit and evaluate a predictive model using held-out evidence and a metric aligned to the decision — write the smallest complete example that exposes the rule.

Explain why the generalisation result works, what would break it and how the program should signal or recover from that failure.

Test with generalisation

Before running an predictive model example, make a trace table with the important state before and after each operation.

Include the value associated with predictive model, the control decision governed by evaluation metric and the output or object affected by generalisation. The predictive model table turns an unexplained result into a sequence that can be tested one transition at a time.

Test three inputs: an ordinary case, a boundary case and an invalid case.

State the expected generalisation result for each before execution, then compare it with what the program actually does. A useful test of evaluation metric isolates one rule; changing several conditions at once cannot reveal which condition caused the failure.

Practise explaining the solution without reading the code.

For DATA1002, name the data representation, the control flow, the responsibility of each function or class and the reason the chosen design supports fit and evaluate a predictive model using held-out evidence and a metric aligned to the decision.

This generalisation rehearsal matters when a written test or interview asks why the program works rather than whether it produces one correct output.

Transfer to Predictive Models, Evaluation and Explainability

A complete response should make the task visible before the detail: identify what must be decided, define the relevant terms, connect the evidence to evaluation metric, and use generalisation to test the result.

The final sentence about generalisation should answer the question actually asked rather than merely repeat the topic.

The controlling limit is specific: Training accuracy and one aggregate metric can hide overfitting, subgroup error and an unusable decision threshold.

Keep that generalisation limit beside the worked example, because it separates a careful DATA1002 answer from one that sounds confident but claims more than the task or evidence supports.

For revision, retrieve predictive model, evaluation metric and generalisation without notes, explain their relationship aloud, then complete a changed version of the application: fit and evaluate a predictive model using held-out evidence and a metric aligned to the decision.

Record the first failed evaluation metric reasoning move and repair it before attempting another case.

What this chapter covers

• 01

  predictive model

• 02

  evaluation metric

• 03

  generalisation

• 04

  Applying predictive model

• 05

  Limits of evaluation metric and generalisation

AskSia practice: apply Predictive Models, Evaluation and Explainability

Key terms

predictive model

A fitted rule using input features to estimate an unknown outcome for new observations. Use this definition when the task is to fit and evaluate a predictive model using held-out evidence and a metric aligned to the decision.

evaluation metric

A quantitative criterion measuring one aspect of predictive performance against observed outcomes. Use this definition when the task is to fit and evaluate a predictive model using held-out evidence and a metric aligned to the decision.

generalisation

The ability of a fitted model to perform on relevant new data beyond its training examples. Use this definition when the task is to fit and evaluate a predictive model using held-out evidence and a metric aligned to the decision.

Predictive Models, Evaluation and Explainability FAQ

Exam move

Reconstruct the relationship among predictive model, evaluation metric and generalisation; complete the chapter application without notes; then test the result against this limit: Training accuracy and one aggregate metric can hide overfitting, subgroup error and an unusable decision threshold.

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