How to understand the dimensions of machine learning?

It would have tied Prado various lines for you before finding the best fit line for each line On this particular dimension length you can take any dimension or for each line on this particular dimension called Lent you would have 100 difference Looks He’s a lame would have had a different slope different coefficient So those coefficients would have a distribution of their own Those different coefficients which he tried out on the length dimension before it found the best foot line.

Those questions would have had different distributions off that distribution The fun one given to you for best foot line minus 93.38 Maybe this one minus 93.38 How far away it is from the central value is given by yours Peace Corps This is Miner’s one point Oh whatever this piece scores What This Peace Corps Peace Goddess Aziz for all if he remembers the schools examiners expert Mr Under Division How many standard divisions of a data point is from the Central Valley View Z score for that Then use students to distribution We call it P scores Formless See Remember students three distribution.

Okay The former Lissy So the selected value for question for land harmony Standard divisions RV It is from the Central Valley for the distribution of coefficients on land There is going with this the standard deviation off this distribution The standard division of this which we call standard error in p distribution that is given by this This is a standard division This is how many’s the scores of a it is that you’re selected Value is from the center Lose this You want to play these two Well to play these two please Well you play these two and tell me what is the value of get into minus 1.24 73.

You’ll get back the same value You’ll get back this value Okay look at this If I’m doing Z score okay These course centralizes your data converted this of the expert in vibrant zeros I take any value Excite Excite minus X bar by standard division is Muzi school agreed Expert is evil because it’s enters the data So that means the school is nothing but excitable standard division So excite is nothing but these got into standard division here we call it B and we call this started dinner So in a respective it started dinner.

It gives him back toward the value that you had over here Excite It gives him back they excite Okay so that is the way to interpret this off All the various coefficients tried out on this length Dimension did select you this one How far over this from the central value If it is too far maybe it’s not a level I’ll do that analysis using this The most important analysis which I want to take you to is the people under What is the speed Will you tell you what the people is telling us I’m going to talk only for length But the same thing applies for all I want to speak values telling us probably is 0.2.

What is it cut off probably for your confidence level 94% 0.55% 0.2 is much higher than 0.5 What this is telling yours even if in the population there is low relationship between the length and the price of the car No relationship with relent and price of the cardinal hypothesis on your taking data from that population you’re likely to find this kind of a relationship between lengthen price in your data which means this is a fluke religion I repeat I want this People is telling this probably finding this kind of a relationship between lengthen price in your later even.

If it’s there’s no relationship in the population in population that the slope is zero between these two and from there you are taking a sample random sample The probably finding this kind of relationship is very high which means this relationship is fluke It’s a statistical fluke It’s not reliable Look at this one Even you uh they look at this one stroke be better is almost zero What it means is if in the real world there is no relationship on board on the price and then from that population you have taken the sample finding this kind of relationship with board and price The problem is very low Doublet is very low I’m talking stroke Sorry Probably This is very low It means this relationship is not fluke It is likely to be there in the real world Okay.

Let me put this pictorially and explain this to you Then we’ll wind up Let’s take one of these columns Stroke and price Same thing applies to all It’s supposed between stroke on price price If suppose there is no relationship between these two then how will the scatter of the data points look Agree All of our value will be very close to zero The scattered the data points Very circular Okay Now what You’ll observe vis most of the data points are near expert Why But I said go away The density false That is why you get a plot like this on this And a plot like this on this Are you all ok.

When I convert this into three dimension bearing the third dimension is my expert by bar If I plot the density on the Y axis question to you is how will their how in the distribution Look I don’t know Cylinder given my very close It’s very high as a movie the density falls So this will be your normal co A three dimensional Momoko A three dimensional laudable curve is a shape off the density of distribution between two dimensions which don’t correlate This is the shape off null hypothesis Now lipo Tosa stays in your universe The data is distributed like this Okay We don’t know what the universe looks like.

Now hypothesis it looks like this If so on from there have taken a sample on in the sample I’m finding the distribution between this core whatever this succour and price I’m finding the distribution like this This is export of a book I’m finding a squashed Bilko I’m not finding a cemetery Belka I’m finding a squashed Volkoff This relationship is borders explosives this coefficient Now the question is what is the problem of finding this kind of a distribution in your later If the reality is like this that problem is expresses fever Lou.

If the people is very law listen 0.5 that means your universe is not like this We reject in Aleppo Texas whereas if people is good and important five we say that there is not sufficient evidence to reject In a hypothesis I’m unable to reject an ally Prosthesis may not be I don’t know but I don’t have sufficient evidence to claim that there is a correlation way we’re trying to establish whether in Aleppo this likely Retour not We can’t do the other way We can’t prove alternate hypothesis It’s a fusion ship already No not like Oh this is no relationship They’re independent It’s always negates your alternate alternatives There is a relationship in Aleppo.

There’s nothing doing this new relationship no relation shipments This is bay Now The question is from this you’re drawn The sample in the sample You’re saying this What is it Probably seeing this kind of distribution if it’s coming from this If the people is listen 0.5 we reject in Aleppo Texas If people lose points verify or greater than equal to be safe we don’t have sufficient evidence to reject an ally prosthesis So with the exception ally POTUS is likely to be true We reject this this analysis are provides My phone doesn’t And if you look at this this is the P values So this tells you the importance of attributes.