In this video, we will learn about confusion matrix after we have trained our model and our model is
predicting the losses of life.
We can make a matrix called confusion matrix, which can tell us the performance of our model.
The Matrix is very simple in the columns.
We put the true value of the glasses.
Like yes or no or defaulted or not defaulted.
In the news, we put the predicted value of response to this.
Yes and no is predicted by our model.
This.
Yes and no.
Is the value which we already have in the training sector.
So not this cell represents where the actual value is.
No.
And when we predicted no.
Therefore, this is a correct prediction by our model.
Similarly, this cell is giving us the cone of cases where actual value is.
Yes.
And we predicted.
Yes.
So this is also correct predictions.
These two cells are giving correct predictions.
And these two cells are giving us wrong predictions here.
The true value was yes.
Whereas we predicted no.
It truly was no.
We predicted yes.
These two errors are guard type one.
And I do it at.
Type one error is when you predict.
Yes, but the actual value is no.
And type two error is when we predict.
No.
But the actual values.
Yes.
It is easy to get confused between type one and type two.
It.
I saw this image somewhere.
Just help me remember.
Which is type one error.
And what is they do it.
They when it it is when are immortalizing that this person is pregnant when that person is not or cannot
be.
I do it.
It is when I immortalizing that this lady is not pregnant when she actually is.
So using this image you can easily remember that type one error is a false positive and type two error
is a false negative.
The point of segregating these two type of error is that often the cost of making each type of error
is different.
For example, if you have a safe full of money, which opens when it detects your face, type one error.
In that case will be if the safe opens up when it is someone else instead of you type two, it is that
you are standing in front of it and it is not opening.
Now, you may accept I do it at that maybe two out of 10 times.
It does not open for you.
You can always replay in such a scenario, but do not accept.
If it opens up for any two out of ten random people, that's not really safe in such a scenario.
The cost of edit is different.
We often shift bond devalue to a more conservative value.
So, for example, your staff will not open when it is more than 50 percent sure, but it will only
open when it has more than 80 percent sure that it is you.
So confusion matrix is a common way to show how well your predictions fare against the two values.
And we are going to learn how to create confusion, metrics and this software package.