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Capstone Project · Lesson 89

Create a Baseline

A baseline is the simplest credible reference performance for the task: majority/mean prediction, simple heuristic or low-complexity model.

ConceptWorked examplePracticeKnowledge check
Textbook walkthrough

Create a Baseline

A baseline is the simplest credible reference performance for the task: majority/mean prediction, simple heuristic or low-complexity model. It verifies the evaluation pipeline and establishes how much value a complex model actually adds.

Learning goal: explain why Create a Baseline behaves this way, apply it to a small example, and verify the result independently. Begin by being able to justify this first step: Fit/evaluate the baseline using the same split and metric as later models; save its predictions for direct comparison.

Deeper walkthrough

Read Create a Baseline as a mechanism, not a recipe

Treat this as a sequence of observable decisions rather than one opaque command. Stage 1: Fit/evaluate the baseline using the same split and metric as later models; save its predictions for direct comparison. Stage 2: Keep the stage inside the same data/validation definitions used by the rest of the project. Stage 3: Save the evidence produced by this stage so the next stage can be audited.

Mechanism

Follow the transformation

Fit/evaluate the baseline using the same split and metric as later models; save its predictions for direct comparison.

Keep the stage inside the same data/validation definitions used by the rest of the project.

Save the evidence produced by this stage so the next stage can be audited.

Evidence

Know what would convince you

  • Verify the split/validation boundary before comparing scores.
  • Inspect model/preprocessing state or a hand-computable tiny example.
Useful distinctionRepresentation: How the method encodes inputs/predictions.
Click a stage to inspect what happens, what changes, and what should be checked before moving on.
Stage 1

Fit/evaluate the baseline using the same…

Fit/evaluate the baseline using the same split and metric as later models; save its predictions for direct comparison.

Transformation focus: keep the input and produced parameters/result separate so the change is observable and reproducible.
How it works

Trace the mechanism step by step

  1. Fit/evaluate the baseline using the same split and metric as later models; save its predictions for direct comparison.
  2. Keep the stage inside the same data/validation definitions used by the rest of the project.
  3. Save the evidence produced by this stage so the next stage can be audited.
Worked demonstration

Create a Baseline evidence

Create a Baseline evidence
Evidence: advanced-model gains are reported relative to the baseline, not in isolation.
Expected / illustrative result
The worked evidence makes the output of this project stage concrete and auditable.
Interpret the result.

For Create a Baseline, trace the specific input through the mechanism above and independently verify one returned value, state change or side effect.

Distinctions & related ideas

Place the concept correctly

RepresentationHow the method encodes inputs/predictions.
Learning/operationWhat fitted state or calculation changes.
ValidationIndependent evidence used to judge generalisation or correctness.
Use deliberately

When it is appropriate

Use Create a Baseline when it answers a defined question in Capstone Project and its inputs/assumptions match the current data or program state.

Boundary conditions

When to stop or reconsider

Reconsider Create a Baseline when the required information is unavailable, the operation would violate a validation/data boundary, or a simpler operation answers the question more transparently.

Common mistakes

Failure modes to recognise

  • Optimising on the final test set.
  • Ignoring feature scale/representation or split structure when the method depends on them.
  • Reporting a single score without checking errors, variance or operating conditions.
Verification

How to check the result

  • Verify the split/validation boundary before comparing scores.
  • Inspect model/preprocessing state or a hand-computable tiny example.
  • Perturb one input/hyperparameter and predict the expected direction or behaviour.
Hands-on practice

Demonstrate understanding

Try this:

Construct a tiny example of Create a Baseline. First fit/evaluate the baseline using the same split and metric as later models; save its predictions for direct comparison. Then keep the stage inside the same data/validation definitions used by the rest of the project. Predict the result before execution and explain one boundary or failure case.

List the stage inputs and expected artifact, rerun it from a clean state, and compare against a concrete acceptance check.
Knowledge check

Check reasoning, not memorisation

Which approach best demonstrates understanding of Create a Baseline?

Quick reference

Remember the logic

Step 1Fit/evaluate the baseline using the same split and metric as later models; save its predictions for direct comparison.
Step 2Keep the stage inside the same data/validation definitions used by the rest of the project.
Step 3Save the evidence produced by this stage so the next stage can be audited.
Lesson summary

What to remember

  • A baseline is the simplest credible reference performance for the task: majority/mean prediction, simple heuristic or low-complexity model. It verifies the evaluation pipeline and establishes how much value a complex model actually adds.
  • Fit/evaluate the baseline using the same split and metric as later models; save its predictions for direct comparison.
  • Optimising on the final test set.
  • Verify the split/validation boundary before comparing scores.