Model Evaluation - lesson 6 | HackIndia Skills

Model Evaluation - lesson 6 | HackIndia Skills | HackIndia

Example

Project description example

Use this as a plain project README or demo script section. Replace the values with your own numbers.

The limitation is not an apology. It shows that you know where your test is weak.

Model evaluation

Dataset: 2,000 labelled support messages from our college helpdesk sample. Labels were spam and not_spam.

Split method: 80/20 random split with a fixed seed. Train: 1,600 messages. Validation: 400 messages.

Baseline: Majority-class baseline predicted every message as not_spam. Baseline accuracy was 72%.

Model result: Logistic regression with TF-IDF reached 89% accuracy on the validation set.

Confusion matrix on validation set:
- True spam predicted spam: 82
- True spam predicted not_spam: 30
- True not_spam predicted spam: 14
- True not_spam predicted not_spam: 274

Key metric: Recall for spam was 73%. We chose recall because missing spam lets bad messages reach users.

Limitation: The data came from one college-style helpdesk sample, so the result may not hold for messages from other campuses, languages, or festivals when message patterns change.

Tip

Use AI carefully

You can ask ChatGPT, Gemini, or Claude to turn your metrics into a clean paragraph. But paste the numbers yourself and check every claim it adds.

AI often makes the result sound stronger than your test. Delete claims about real-world performance, production readiness, or users you did not test.

Prompt:
Write a short, honest model evaluation section for my hackathon README.
Use only these facts. Do not add claims.
Dataset: [describe]
Split: [describe]
Baseline: [score]
Model: [score]
Confusion matrix: [numbers]
Key metric: [metric and why]
Limitation: [limitation]

Write an honest result

One honest result

What to include

Write the section

Project description example

Do not overclaim

Use AI carefully

Do this now

Quick check