When I started with Data Mining 101, the first thing I came across was known as “The Confusion Matrix”. It seemed interesting at first so I started diving deep. It looked very simple at first, but then when more terms were introduced, things become complicated rather confusing; and then I realized the reason it is called **“The Confusion Matrix”**.

The terms True/False Positives/Negatives are really very confusing to remember and hence I found a sleek way to understand it.

Just add “ly predicted as” between two words

So, True Positive → Truly predicted as Positive False Positive → Falsely predicted as Positive True Negative→ Truly predicted as Negative False Negative → Falsely predicted as Negative

## Interpreting True/False Positive/Negative

**True Positive:**
The data point is a True Positive if it is `truly`

predicted as `positive`

. If I try to elaborate `truly`

we get something like this: The data point is a True Positive if it is actually positive and is truly predicted as positive.

For a food lover:

When a Burger is predicted as Burger and not Hotdog it comes under True Positive.

- Burger - Positive Class
- Hotdog - Negative Class

For an animal lover:

When a Dog is predicted as God and not Cat it comes under True Positive.

- Dog - Positive Class
- Cat - Negative Class

**False Positive**:
It will be interesting when we try to interpret False Positive.
The data point is a False Positive if it is `falsely`

predicted as `positive`

. If I try to elaborate `falsely`

we get something like this: The data point is `False Positive`

if it is actually `Negative`

but it is `falsely`

predicted as `positive`

.

Again, for a food lover:

When a Hotdog is predicted as Burger, it comes under False Positive

Again, for an animal lover

When a Cat is predicted as Dog, it comes under False Pos-itive

Rest you can derive on your own 😉

## Conclusion

In conclusion I would like to say … add “ly predicted as” in the middle.