What are linear models and problems associated with it?

What we did yesterday So the line will look something like this So this line why equal toe wise property of belonging to blue class is related to M X X is variable plus c sees the intercept Aurea seem busy does even be negative But the problem with the lean in mortal iss linear models go to infinity minus plus probably these have to be thin Zero and one.

I’ll be below zero Horrible one So we can do Selenia The model views another technique There we transform this lying into an s curve This Esko is scored a sigmoid and it is very easy to achieve this The Sig mind is nothing but one by one plus e ease Oiler constant views and mathematics miners MX plus c So this best freak line the best front line which is formed for you This breakfast line is fed into this transformation This mathematical formula that isn’t of this transformation Is the school the broad Is it read Zeroed infinitely it’ll reach one That’s exactly the program Beautiful program.

Let us interpret Want this for molesting Keep in mind we’re building the models of which means we’re in training zit So in training that we already know the labels off every record right The algorithm gets bored The input variables and output variable x and y So all the labels are already known on the labels are shown in the color or you Are you all OK Shall we proceed now What The mortars know that I don’t sigmoid I’ll remove this.

This line I’ll remove this line Okay This is going to infinity You don’t even need this We don’t even need this No What This Ah Morgan is telling you next Look at that If you think this is a great point look at this If you take this rate point want to model is saying is the probability of belonging to blue class What is the property of this guy Suppose we don’t know the color What is probably at this point belonged to blue class.

It’s very low which means it belongs to the red class So the model prediction and actuals are matching here The mortal prediction on the actual label Both of them are matching Take this green One born the model is saying is what is the property That this brain point belongs to blue class This is probably blew class right The problem is very high and he’s actually green So both prediction on actuals match Hey lady are you with me Sure 100 per cent Okay.

Don’t get left behind your expressions Tell me a lot of stories I’m going to move there and I lost count more than 250 stations No indeed A science So I built a model Uh uh You know what’s coming right So looking at that place the position people occupy in the class I can guess lot of things about the person believe me the old full of patterns and your you’ll be surprised and no distance pattern without based on the position people are occupying the class Okay of course Late comers.

They don’t have a choice But people who come early okay Based on the potions they occupy I can draw a lot off conclusions inferences from right So people who are feeling doubtful but our ableto ask their fear They’re not asking I know I cannot in for such people so feel free to ask Okay All of you please feel free to us Don’t get left behind I was always a backbencher It was always a member of the backbenches Sadie I’m not saying anything to you Don’t look at me if you if you sit in all places means I was always the back benches so I know Moreover the back bench and then the friend print But other patterns with your absurd was doing decisions right.

A deviation from our main topic of discussion Bottom Linus don’t hesitate to ask Okay Feel feel free to ask Don’t respect coming back to this Are you all with me on this tour Discussions Okay No Look at this point You see a red point here in this one bought the mortal is thing is as Paris income he belongs to Probably if he belonging to blue class is very high So the Morgan live possum is blue I started this guy’s green green in this case green So what the models are faces telling is predicting the problem of belonging to the green class It’s very high So the modernist classic famous green.

But he’s actually read The model is done An error over here Mystic Similarly if you look at this one this side the green point here Can you tell me what the model is telling you The modernist Leo The problem of them belonging Toa green class is very low so he’s likely to be read but actually is green So the model has done an error here A mystic Okay so whenever you have overlapping data sets as in this case in spite of the best foot line the classifications You’ll find some mirrors They will always bears thes errors on core training Its errors in the training data I like you know uh beach on his mind This one this is Eagle the zero This is one on this axis in this particular picture This access is probably have belonged to class work.

Okay that’s why your escort is coming like this This s come has been built out of a linear model If we had taken this if you’re taking this as probably belonged to class zero then it will be reversed This cup because you’re covered like this the line would be like Oh yeah all more than separate without Let me answer the question Why is way way orphan Who said that In a linear mortals we don’t have any boundary They go to plus infinity minus thing for me we don’t want that Probably is have to be between zero and one.

That’s where they were converting The Signora Signore curve has that property that low vis remained between zero and one severely Remember the map the numerical values to probably function so yes in multi class classification it’ll be one versus rest Suppose I used to think this logistic regression for OCR optical character recognition So you’ll have one esque of for a worse is others one s curve for B versus others So on so forth So on your mathematical space you’ll have my people s curves cutting each other One is for a versus others Otherwise be vessels of their CVS isn’t so on so forth All these algorithms Decision tree.

I be a support vector machines noodle It works modesty They all break your mathematical space into compartments the break them into compartments pockets where one pocket belongs to this object The other pocket belongs to that object All of them You’re done Decision to yesterday which doesn’t do to use that is the human evidence Dennis it Okay That is a binary classification actually So when your big decision trees Okay this is a structure to have off position trees What you’re doing here is this entire mathematical This place is your route Lord The entire data said all data points put together a sea route note.

You find out one particular column on which you break the date under two that is like putting a vertical boundary here So these two no are these childhoods But you have not achieved your uh vertical home virginity Your genie index is still very high or in tough is very high It’s still not pure leaf So what you do is you again find a column here in this data set on break this into two That means you’re drawing a vertical boundary So this on this is the child not here in here So decision tree also critics of mathematical space into pockets So start each pocket becomes homogeneous at least level So right now I have reached the leaf level Hopefully at least level.

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