In this video, we will discuss about the bias media straight off.
So as I told you in the test, trains split Lichter, our agenda is to find the model the lowest.
Test it a.
Now, fundamentally, there are three contributors to the expected tested at.
These three contributors are called variance bias and the variance of erratum, which is represented
by IDO.
This totem.
Comes from the fact that there is some inherent randomness in the process and given sambal observations,
also do not follow the intended function.
So this is an irreducible error.
And since we cannot do much about it, we will not focus on it.
We'll focus on these two other terms and let's talk about them one by one.
Towards variance variance refers to the amount by which efford change.
If we change our training dataset.
And bias refers to that part of it, which is introduced by approximating a complicated Real-Life relationship
with a simpler model.
So let's look at them one by one.
So as I told, new variance refers to the amount by which the predicted function would change.
If I change, my training, does it.
If you remember when we talked about simple linear regression, I told you that there is this group
Population Lane used by this recall, which is the best line if we were putting the line on the whole
population.
But when we have putting it on a sample, the sample regression line is different from the population
regression line.
And different sample data changes, the sample regression line also changes.
So basically variances capturing the part of error which is coming from that particular simple.
So if we have two models, one of them is more flexible than the other.
Which one will have more variance?
Well, since the more flexible method, we'll be trying to touch each and every point.
Even if I change one or two points, it will give out a completely different that it would function
to accommodate this small change.
This means that more flexible methods of high variance.
This is shown graphically as well.
This first graph on the left.
We are trying to predict this relationship with a straight line.
Straight lane is a very less flexible matter.
Even if I change one or two data point, this blue point.
These lope and the intersect of this line will not change as much.
However, if you look at the function on the date, if I change even one or two point on this go.
The predicted output function will be very different.
So you can see that the variance is very high.
If the flexibility and the covers I saw more flexible Dimitar, I will be the brilliant.
This phenomenon of following the data too closely, as you see in the right glove.
That yet even following the error in the observations is called Overfitting.
When we overdo it, we do get low training error.
But the testator increases.
Now, let's talk about bias bias refers to that part of the error, which is introduced by approximating
a complicated Real-Life relationship with a simpler model.
For example.
He may be trying to fit a linear model between dependent and independent variables with a linear relationship
is highly unlikely.
You can see in this graph the points can never be fitted with a straight line.
Much still.
But still, if we select a linear model, it is always going to have some error.
And that part of it, it is called the bias.
And how is bias littered with flexibility of model?
You can see that linear model, which is less flexible, is unable to fit this data.
If I increase flexibility and allow it to go, then it will better fit the point.
So generally, if we increase flexibility, the bias error reduces.
So you can see where the bias variance tradeoff is coming from.
As we increase flexibility, error due to variance increases and error due to bias decreases.
Although we want to decrease board, but when we do a two degrees one, the other one starts to increase.
So the challenge is to find that point where this summer's minimum.
This is depicted graphically here.
This orange line is showing us the variance, which is increasing with flexibility.
And this blue line is for bias, which is decreasing with flexibility.
And this red line is some of the of these Twitters.
We want to find this minimum point that this sum is the minimum.
Although we will not be able to compute bias and variance for our model.
This concept will be used when we will be comparing different models and their potential accuracy in
predicting dependent variables.