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Hey.

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And welcome back to chapter eleven point one where we go into the confusion matrix and then calculate

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precision and recall.

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So let's get started.

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So before I dive into Python that book and we actually would you an example using sikat confusion matrix

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function.

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Let's take a look at what the confusion matrix actually looks like.

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Now typically which I shown you before.

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You have basically true true positives true negatives false positives and false negatives.

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And that was in a binary class analysis.

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Now I'm going to go a step further and make you understand this concept using a multiclass problem and

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we're using actual real world results for amnesty the set.

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So this big worry kind of looks like this big matrix looks a bit strange.

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However you do pick up initially that is a pattern right here.

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That is a diagonal way.

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These are very large numbers and then there are small numbers on the outskirts.

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What do these numbers actually mean.

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So let's take a look now.

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I've made it simple.

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We know we're looking at amnesty to set.

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So we have 10 classes that's 0 2 9 9.

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Likewise here.

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And what this call them here is the predicted value.

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So these these numbers here mean that classify a predicted zero and true value was actually zero.

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So that's why it is a big number here.

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So to one shoot value was actually 1.

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So having large numbers in this diagonal is a good thing.

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Having large numbers outside of his batting so we can see we have some large numbers here.

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We have an 11.

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We have six to five here.

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So now let's take a look at these numbers so I've highlighted them here.

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So what do they actually mean.

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It means that our classify predicted to when it was actually 7.

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So classify as confusing systems with two is it seeing a seven but it's classifying it as a two which

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is wrong.

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And likewise for sixes and zeroes you know nines and fours here.

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Sorry this one here.

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And it's as well.

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So what I mean is that let's look at nines and fours classify a predicted four but five times it was

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actually a 9.

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So we can see the biggest problem.

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Oh emulous classify as facing is confusing to his and sevens which is actually a real problem for a

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lot of humans.

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My handwriting isn't very good.

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I'll be the first one to admit that.

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And lots of times I'm looking at numbers I write and I'm like Is that a 2.

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Was it a 7.

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So we can see all calcify sort of leaning generally like a human with lean and interpreting all misinterpreting

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results just like a human like ourselves would.

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So let's actually work out or recall value based on this real world data.

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So let's take a look at the number seven.

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All right.

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So this is the true classes for number 7 here.

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So we saw a classified got it right.

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One thousand and ten times that's true positives here.

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So how do we get the number of false negatives.

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Now for times number of false negatives.

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Basically how many times are classified I predicted a number would have been a number that was supposed

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to be a 7.

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So you can see all these all these crimes here.

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Numbers were supposed to be a seven but it was actually predicted to be indifferent to us like 0 1 2

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especially.

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So let's sum this up.

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This rule here everything here except to tell then we'll give you 18.

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And that's exactly what we calculate here.

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And we get ninety eight point to four percent and that's our recall.

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And now let's move on to precision.

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So looking at precision we know it's number of correct predictions over how many occurrences of that

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class when the test data set.

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That's another way of seeing what this is here.

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True positives over true positives Plus are false positives.

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So again let's look at number seven.

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OK.

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So now we go the other way.

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This is interesting.

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So are true positives again.

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It isn't 10.

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And what about false positives or false positives here.

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Basically all this time the classify was predicting something to be a 7 when it was actually 0 to a

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tree above or below.

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So those are the false positives for sevenths and we just sum this up everything here gives us a thousand

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seventeen.

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One two tree and then three plus four.

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It seems that that's exactly how we get ninety nine point one percent so basically we don't actually

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have to do it manually.

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So I could learn actually does it generate generates a report for us automatically that gives us record

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precision F1 and support.

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I think you remember and you guys know what one is.

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I haven't actually dealt with support but I'll talk about it now in the next slide.

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But you can see we have precision we have recall and we have all one score here.

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All right.

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Now actually I can talk about support right now.

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It's actually quite easy.

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Support is basically you see the numbers here.

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Ten twenty eight.

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What was that.

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Go back to it here 10 20 it is basically true positives plus false negatives here.

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So support just gives us that some here in the column.

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So if you look at column 0 Let's go back to it.

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Column zero would be everything here.

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Sorry.

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Everything in disarray.

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Added up here.

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So this is 977 plus 1 2 3 980.

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And that gives us our support here and support.

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It basically is useful.

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How many times of classify is actually basically missing data essentially missing classifications because

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think about it intuitively we have nine hundred and eighty zeroes represented here in US in this report.

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All right now what's what is telling us here is that we basically had a that's how much is zero.

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How many zeros were in our report.

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So now we can actually use that as a basis to can gauge what is happening here.

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So you can also see if any class imbalances in this are in the data here.

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So what about this and imbalances are essentially which you can usually check before you even reach

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this point.

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You can easily check it when you have your test entering data.

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Just check to see how many quantities it seems are the conchs I should see of data of each class of

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data.

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You have no data set.

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So how do we analyze overclassification report.

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So basically we can just quickly interpret something here high record with low precision that is bad.

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And let me tell you what this tells us that most of the positive examples are being correctly recognize.

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That means a lot of false negatives but there are a lot of false positives.

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And that means a lot of the other classes have been predicted.

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As a class in question and Alternatively we can have lower recall with high precision.

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What does that mean.

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It means are classified as missing a lot of the positive examples has a high false negative rate but

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do as I predict as positive on the positive side of false positives.

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So we can use our prosecution classification report to sort of gauge what's actually happening.

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In this example everything looks pretty good.

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All right.

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But later on you look at some examples where we generate these reports and you can actually analyze

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and figure out which class overclassify is having trouble with.

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So let's quickly take a look at the code to generate this confusion matrix.

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All right.

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And later on we're likely to see him could look at misclassified data for now this is the generic administrating

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code.

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You've seen a few times before it's using a lot of my examples.

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There's one thing I wanted to show you here in this file.

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Basically when we see of we still history here some people have asked me How can I can I see if my history

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file.

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And look at it again because I've spent like hours or maybe a week or days training a classifier and

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I want to actually see if the plots were not seeing the image when I should see the file.

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Yes you can you can use pite one function called pickle.

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And basically what it does here just stores a file as a pickled file pickle file is basically an array

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of data as a method of storage.

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I'm not going to get into the detail now but just know it's a way we can store files.

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So what we do here we just pick allowed to create a file we've been it's basically us telling us we're

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going to create this pickle file and then we just dump this file.

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This is some history to history file.

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We want to save and then close the file and it's done.

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And similarly if we want to look at this file because it's simply just loaded back here and here we

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go.

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Well we have all just for one epoch there was more than one book it would look a lot bigger.

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But basically it's a dictionary file or just on file.

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For those of you who come from a javascript background.

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And basically this is how it looks.

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We have a loss accuracy validation accuracy of Allision loss and values for it here.

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As a key these are the keys and these are the values.

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So now we can get some plots but these plots obviously for one epoch are pretty much not fun to look

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at.

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Just one point here and an accuracy chart one point here and one point here that one can actually see

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it's really quite good.

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This is what I wanted to show you.

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This here is a good fusion matrix at ossification report.

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So we import from Escaflowne metrics.

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Both of these functions here.

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And basically what we do we just get our predictions here.

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So we run ex-s accesses or test data or validation data through our models are pretty classes and basically

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we just print out the classification or report classification report just takes two arguments.

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We tested the X test labels here to White US labels here and predictions.

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So basically what we're doing here we're comparing labels to labels.

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And the reason we have to use the max function is because our labels before will not want encoded.

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So it's not like for like we actually have to knock reconvicted back into basically a one for one type

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matching.

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So this is basically what our protection matrix give us.

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And basically this is what the classification of what looks like which we saw in our slides.

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Are It averages here.

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They don't really tell us that much closer but the same may be far more interesting datasets and of

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course those values would differ and confusion matrix is done here.

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Basically the same thing same exact arguments as above and we get it here.

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So that's it for confusion metrics and our misclassification.