UNIT 2 · LESSON 7 · 60-MINUTE SUGGESTED PLAN

Data में किसकी कमी है?

Identify missing representation in a dataset.

Understand

A dataset can be large but narrow. A recogniser trained mostly on bright daylight pictures may struggle in dim light. Look at which conditions are represented, not just the total number of examples. Do not collect faces or classmates’ personal information for this activity; paper object cards are sufficient.

Original classroom scenario / sample claim
A fictional leaf recogniser uses 1,000 photos, all from one tree in sunlight. A claim says the quantity proves it works for every leaf.

Try it and check it

List conditions missing from the dataset: species, lighting, angles and damaged leaves.
Design a balanced paper test set across at least three conditions.
Explain one likely failure and the evidence needed to investigate it.
Rewrite the universal claim using an appropriate limitation.

Without a device: Print this lesson, use paper cards or the supplied fictional text, and discuss the checks in pairs. A student can later enter their own notebook record for teacher review when a device is available.

Notebook: write your corrected or unresolved conclusion, the evidence you actually checked, and a reflection. Do not include passwords, identity documents or private information. Original BrainCheck companion content, BCAI-v1.

QUICK CHECK • PRACTICE FEEDBACK

Which change most directly improves coverage?

Duplicate one existing photo many times

Add varied relevant conditions and labels

Increase the title font

Hide every failure

Write notebook entry Print worksheet / Save PDF Next lesson →