August 23, 2017

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 Dog 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 Positive

Rest you can derive on your own 😉

## Conclusion

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