Classification
All MATLAB topics∙ MATLAB
Classification explains supervised prediction of discrete labels from measured features. You will learn the exact MATLAB behavior, implementation rule, failure mode, and verification evidence for this lesson.
Syntax
% Topic: Classification
model = fitctree(features, labels);
prediction = predict(model, sample);Example
% Topic: Classification
features = [1 2; 2 3; 8 9; 9 10];
labels = categorical({'low';'low';'high';'high'});
model = fitctree(features, labels);
prediction = predict(model, [8.5 9.5]);
disp(prediction);Expected Output
highLine-by-line
| Line | Meaning |
|---|---|
% Topic: Classification | Builds the data or operation used by this MATLAB example. |
features = [1 2; 2 3; 8 9; 9 10]; | Builds the data or operation used by this MATLAB example. |
labels = categorical({'low';'low';'high';'high'}); | Builds the data or operation used by this MATLAB example. |
model = fitctree(features, labels); | Builds the data or operation used by this MATLAB example. |
prediction = predict(model, [8.5 9.5]); | Builds the data or operation used by this MATLAB example. |
disp(prediction); | Displays the calculated result. |
Real-World Uses
- 1Classification is used when a MATLAB workflow needs supervised prediction of discrete labels from measured features.
- 2Its exact implementation rule is: Separate training and test data and choose metrics appropriate to class balance.
- 3A practical classification workflow defines inputs, units, expected output, and validation criteria.
- 4The main production risk is: Evaluating on training data exaggerates classification performance.
- 5Teams evaluate it using held-out classification quality.
Common Mistakes
- 1Evaluating on training data exaggerates classification performance.
- 2Implementing Classification without understanding supervised prediction of discrete labels from measured features.
- 3Ignoring dimensions, orientation, units, or missing values in the classification workflow.
- 4Skipping the verification step: Check confusion matrix, class metrics, and predictions on held-out samples.
- 5Optimizing before collecting held-out classification quality.
Best Practices
- 1Separate training and test data and choose metrics appropriate to class balance.
- 2Document supervised prediction of discrete labels from measured features with the smallest useful MATLAB script, function, class, app, or model.
- 3Validate the dimensions, types, units, and assumptions required by Classification.
- 4Check confusion matrix, class metrics, and predictions on held-out samples.
- 5Use held-out classification quality to guide further changes.
How it works
- 1Classification relies on supervised prediction of discrete labels from measured features.
- 2Separate training and test data and choose metrics appropriate to class balance.
- 3Its main failure mode is: Evaluating on training data exaggerates classification performance.
- 4Useful production evidence is held-out classification quality.
Implementation decisions
- 1Choose the owning script, function, class, app, live script, or Simulink model.
- 2Keep the classification input shape, units, and output contract explicit.
- 3Select MATLAB data structures and toolboxes according to the exact operation.
- 4Document release, toolbox, hardware, and file dependencies.
Verification plan
- 1Check confusion matrix, class metrics, and predictions on held-out samples.
- 2Test normal, boundary, invalid, noisy, empty, or missing input where applicable.
- 3Compare one result with a manual calculation, analytical model, or trusted reference.
- 4Record held-out classification quality before and after changing the implementation.
Practice task
- 1Build the smallest working Classification example.
- 2Introduce this failure: Evaluating on training data exaggerates classification performance.
- 3Correct it using this rule: Separate training and test data and choose metrics appropriate to class balance.
- 4Record held-out classification quality before and after the correction.
Quick Summary
- Classification works through supervised prediction of discrete labels from measured features.
- Separate training and test data and choose metrics appropriate to class balance.
- The key failure to avoid is: Evaluating on training data exaggerates classification performance.
- Check confusion matrix, class metrics, and predictions on held-out samples.
- Measure success with held-out classification quality.
Interview Questions
Q1. What is Classification used for?
Answer: It is used for supervised prediction of discrete labels from measured features.
Q2. What implementation rule matters most?
Answer: Separate training and test data and choose metrics appropriate to class balance.
Q3. What failure is common with Classification?
Answer: Evaluating on training data exaggerates classification performance.
Q4. How should Classification be verified?
Answer: Check confusion matrix, class metrics, and predictions on held-out samples.
Q5. What evidence shows that it works?
Answer: Collect and review held-out classification quality.
Quiz
Which practice best supports Classification?