Imagine we are playing a game.

There is a big box of toys:

• 🐶 Dogs
• 🐱 Cats

Your job is to find all the dogs.

Let’s say we have:

• 6 Dogs
• 4 Cats
• 10 Toys Total

🎯 1. Accuracy — “How many did I get right?”

Accuracy means:

Out of ALL the toys, how many guesses were correct?

Example

You guessed:

• 5 Dogs correctly
• 3 Cats correctly
• 2 mistakes

Table: What Happened

You got:

8 correct out of 10 total

Accuracy Formula (Simple)

👉 Accuracy looks at everything together.

🧐 2. Precision — “When I say DOG, am I right?”

Precision asks:

When I say something is a dog… how often am I correct?

You said “DOG!” 6 times:

• 5 were really dogs ✅
• 1 was actually a cat ❌

Precision Table

Precision Formula

👉 Precision cares about not making false alarms.

It asks:
“Did I call a cat a dog?”

🔎 3. Recall — “Did I find ALL the dogs?”

There were 6 real dogs.

You found:

• 5 dogs

You missed:

• 1 dog

Recall Table

Recall Formula

👉 Recall cares about not missing things.

It asks:
“Did I forget any dogs?”

⚖️ 4. F1-Score — “Can I be good at both?”

Sometimes:

• You are very careful (high precision)
• But you miss many dogs (low recall)

Other times:

• You find almost all dogs (high recall)
• But you call many cats “dogs” (low precision)

F1-score checks:

Are you good at BOTH precision AND recall?

Balance Table

If both precision and recall are high → F1-score is high.

If one is low → F1-score goes down.

⚠️ Why Accuracy Can Trick Us

Imagine:

• 100 toys
• 95 cats
• 5 dogs

You say:

“Everything is a CAT!”

Results Table

Accuracy

😲 95% sounds amazing!

But…

You found zero dogs.

That’s why we need more than just accuracy.

🧠 Simple Summary Table

🌟 Final Idea

Think of it like this:

• 🎯 Accuracy = “How good am I in general?”
• 🧐 Precision = “Am I careful?”
• 🔍 Recall = “Am I missing things?”
• ⚖️ F1-score = “Am I balanced?”

Even big computers use these ideas when:

• Detecting spam emails
• Finding diseases
• Recognizing faces
• Catching fraud

And now you understand them too 😊