It means millions off the total variance In the data set I told you one part of the variances cards by the deterministic The other part of the variances calls by the stroke Castaic long deterministic Something non deterministic is your residue later Some of spirit I think in this case I’m sure you may leave one point But you have to imagine all the points together because this S s e s s sir there across all points put together What I’m saying is what contributes stresses are necessary That’s what I’m sure.
You have to imagine all the points if that becomes too cluttered What contributes stresses are what contributes traces See that’s what I’m showing you Yeah he scares me I told her little Miss this I want to minimize this unexplained there That is what my objectives that great in dissent works in finding the best fit line for you Their best foot line is that line where this terrorist minimized because I don’t want a model which is not able to tell me.
Why I want a model Ish tells me why this is so You know I go to do the professional No because Mister I don’t like civilians It’s because of the natural radiance in the process Okay When you’re running a process many things come into play a factor summoned to play because of its values change here under around the expected value That is okay with me But for again Elif input there multiple values of why that I don’t understand.
So forgiving aloof in production we’ve only one value of why Why are there multiple Well is a way that I don’t want that part is my unexplained it I want a model which minute unexplained interest minimized I don’t want a model where unexplained that it is very high What is the use of the more It will not predict anything Okay You think given milieu that you have one So the dishes is that Peter’s predictive value But here multiple values off p once spread it on the line That gap is unexplained.
What we called stochastic random billions You don’t know why it happens So basically the idea is to minimize SNC that’s all The bottom Linus minimus ecstasy So increase the Isis are assisted issue That is what our objective It’s all right Let more So before you build the model you have to evaluate each independent dimension and see what is our value Our value coefficient of correlation comes into play to help you identify good predictors during the target Okay Once have built a mortal I want to see how good the model is how the level the model lists for that views Another metric that metric is called coefficient of determination Munition on this is represented as our square.
But before you jump to it I should caution you Our square is not always our into our r is a question of correlation so naturally to think that our school will be out into our that is true one Lee Then you build a simple linear model between one damage in one target It’s only true at that point in multi dimensional multi variant analysis very have more than 23 dimensions You can’t see our Squire’s ardent o R.
Okay so ask where is just a symbol that represents 11 metrical for your determination how much off the total variance in your why has been explained by your model Keep in mind the total variance and why is consisting of two components deterministic non deterministic stochastic random How much of this total variance in your way has been explained by your model That measure is called Coalition of Determination obviously a model which captures explains maximal off Bavarians and why it least behind very small residue Lt’s that model is the best for you.
So are square It ranges between zero and one zero means your model is in order to explain anything absolutely useless One means you’re immortal is able to completely explain all the variance in your target There’s no residue Such mortals don’t exist We can never build Such models usually are really will be between zero and one The closer they’re toe won the bet of the modern lists because uh it’s in simple ian models It’s a square term It can never be negative But keep in mind in Multivariate analysis it’s not just a square It’s something more than so long this range But indeed are you all.
How much how much money is in white If this is that means your model is able to explain 80% of the total aliens in your wife total variance which includes stochastic on deterministic So you’re Oscar should be as close as possible to one your model Very good So this metric is used to evaluate the performance of the mortal while our is a metric which is used to evaluate the independent dimensions building the morning geometrically Okay if you take one simple linear model off one X and one way Okay That’s what I’m showing here.
This is where your expert and y bar meet Please Look at this carefully This is my wife But this is my expert Okay so there’s an actual data point Excitement is expert is this way I minus y But is this that gives us an area rectangle We saw that early Right That area represents the total Raelians In your data point the total variance in this data point the co variance in the data point because off X and boy this is that area The light gray area the light gray areas This it’s behind this dark grey You get the light Grady is this.
That light gray area is a germ A trick representation off the total variance because off X and because of why in this data point off this total Raelians for the gin mill of ex immortalised Pretty thing of value of quite a bit This So this predicted value Why minus y bought exits Bar this dark area is the explain billions How much of this total variance has been explained Captured by your model our square is the issue of the dark area Too late area what you’re seeing on the talk This is the residue Lt’s unexplained Williams.
Our Squire is again an issue We have their issues between the total variance in the data points on dhe The variance explained by your model that ratio is scored are obviously the lesser the residuals unexplained variance the better of the modern This way Oh I think you innocent The problem is go back to our discussion on our where I told you if there is no relationship within these to the target and they live in variable you have to give get a perfect symmetrical distribution But the problem is getting a perfect symmetrical dissipation is zero which means you’re going to get a symmetrical distribution.
The moment you have asymmetric distribution you’re our little be greater than zero magnitude this way or that way which means our square will be greater than zero are square It’s not considered as a very good metric for evaluating the models because it is easily impacted by fluke relationship So we make use off another metric Their school adjusted our sport which is nothing but our square minus fluke There is a formula for this.
How do you find out the flu content which I’m going to avoid right now because it’s too detailed to discuss in case you’re interested Let me know I can share it with you Conceptual is easy to understand but the argument is very lengthy We won’t have time for bed That’s I’m going to ignore it right So the metric that will you make use off to evaluate our models is adjusted Our square knot are square the beauty of a distant.