This we do, we learn about other types of media models.
Know, we have discussed the standard linear model given by this equation.
We digress to find out the values of the bee, does bee, does it all be done?
So until beat up Bee and from that regard, predicted values of light.
The sum of squares of the difference of predicted lay and the actual way or the important quantity we
defined.
And we named residual sum of squares.
And we minimized odysseys.
Which is why the more was called or Mary Lee Squeers.
Now we are going to explore some other models other than the plainly squares model.
So there exist alternative voting procedures, which you do benefits.
One is of prediction accuracy and other is models, interpretively.
Prediction accuracy of Li Square method is usually good.
True relationship between predictors and response is approximately linear.
And we have a lot of observations to the grace.
Particularly if the number of observations are much larger than the number of levels.
We may not need any alternative approach.
However, in case if the number of observations is not much larger than be.
Then there will be a lot of variability resulting in a overfit.
And does your predictions.
And in case B is greater than N.
That is number of variables is more than the number of observations.
There will be infinite variability, that is, there will be infinite number of solutions available.
In such a case, by reducing the number of variables which will be selected to run the model are shrinking
those estimated coefficients towards zero, we can substantially reduce the variance.
This small change will lead to a substantial improvement in the accuracy of the prediction.
Second thing is model, interpretively.
If we have irrelevant variables in the analysis, it will unnecessarily complicate deserting model.
If we remove these variables, the model will become more interpretable.
And when do we drop a variable?
If the coalition BDA of that variable is zero, we say that that variable has no impact on the response.
So we can drop it.
But ordinarily, Square's method rarely gives any bit.
That is little.
If our model is able to shutting down the coefficient of not important variables to zero.
You'll be able to rob them.
And that is early model will make more sense to us than.
So this process of excluding irrelevant variables and keeping only the relevant ones is called variable
selection.
In the coming videos, we learn some important methods.
Let's give us these two benefits.
We will be discussing two types of methods primarily.
One type is called subsects election.
In this method, we use a subset of B variables in our model.
Instead of using all the variables.
The second type of method is called shrinkage methods.
In these type of method, we try to string Dondi coalitions of variables to zero.
This is also known as regularisation.
So in the coming videos, we will look at alternative models, which may increase model accuracy and
interpretively.