Welcome to this short 
introduction to Precision, Recall and F1.
You might have come across these terms 
when reading about classification models and machine
learning, but basically, they're all 
ways to measure the accuracy of a model.
When you build a model to predict a certain
class or category, you need a way to measure
how accurate the prediction's are.
This is what precision, recall and F1 do.
They measure the classification model's accuracy.
In our video on the confusion matrix, we 
learned about true positives and negatives, and false
positives and negatives.
This is how many times a model 
correctly or incorrectly predicts a class.
Precision, recall and F1 use these to measure
a model as making many mistakes, when predicting
class, or if it's doing a pretty 
good job at being spot on in its predictions.
But precision, recall 
and F1 measure different things.
so lets break it down into each of their 
parts and the role each play in measuring a model's
accuracy.
Let's say your classification 
model predicts apples and bananas.
If your model avoids a lot of mistakes in 
predicting bananas and apples, then your model
has a high precision.
Likewise, if your model avoids a lot of 
mistakes in predicting apples as bananas, then your
model has a high recall.
You want your model to aim high in both 
precision and recall, where your model avoids as many
mistakes as possible, doing a good job at
correctly predicting both apples and bananas.
But what if your model aces the ability 
to predict one class and sucks at predicting
the other?
Wouldn't it be misleading to 
look at precision or recall in isolation?
This is where F1 comes in.
It takes in to account both precision and recall.
A balance of the two is what F1 scores on.
If your model does a good job at 
accurately predicting both apples and bananas, then
it will have a high F1 score.
There are some cases where you might 
want to focus on precision more so than recall,
and vice versa.
For example, Class A might be an 
aggressive type cancer, and Class B might be no cancer.
The stakes of misleading cancer as no 
cancer or overlooking the cancer can be extremely high.
Therefore, you want your model to avoid 
mistaking cancer for no cancer, or mistaking a for b.
This means you want to focus on recall.
You don't want your model to say 
"whoops, I missed the cancer, sorry!"
You want your model to say "I got the cancer,
maybe I was overly cautious and had mistaken
a few no cancer patients for cancer 
patients, but isn't it better to have a false alarm
of "you don't have cancer after all!" rather
than "sorry, you actually do, but we missed it."
And that sums up Precision, Recall and F1.
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found this useful, or you can check out our
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