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This we do, we learn about other types of media models.

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Know, we have discussed the standard linear model given by this equation.

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We digress to find out the values of the bee, does bee, does it all be done?

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So until beat up Bee and from that regard, predicted values of light.

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The sum of squares of the difference of predicted lay and the actual way or the important quantity we

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defined.

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And we named residual sum of squares.

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And we minimized odysseys.

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Which is why the more was called or Mary Lee Squeers.

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Now we are going to explore some other models other than the plainly squares model.

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So there exist alternative voting procedures, which you do benefits.

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One is of prediction accuracy and other is models, interpretively.

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Prediction accuracy of Li Square method is usually good.

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True relationship between predictors and response is approximately linear.

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And we have a lot of observations to the grace.

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Particularly if the number of observations are much larger than the number of levels.

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We may not need any alternative approach.

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However, in case if the number of observations is not much larger than be.

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Then there will be a lot of variability resulting in a overfit.

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And does your predictions.

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And in case B is greater than N.

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That is number of variables is more than the number of observations.

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There will be infinite variability, that is, there will be infinite number of solutions available.

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In such a case, by reducing the number of variables which will be selected to run the model are shrinking

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those estimated coefficients towards zero, we can substantially reduce the variance.

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This small change will lead to a substantial improvement in the accuracy of the prediction.

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Second thing is model, interpretively.

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If we have irrelevant variables in the analysis, it will unnecessarily complicate deserting model.

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If we remove these variables, the model will become more interpretable.

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And when do we drop a variable?

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If the coalition BDA of that variable is zero, we say that that variable has no impact on the response.

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So we can drop it.

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But ordinarily, Square's method rarely gives any bit.

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That is little.

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If our model is able to shutting down the coefficient of not important variables to zero.

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You'll be able to rob them.

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And that is early model will make more sense to us than.

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So this process of excluding irrelevant variables and keeping only the relevant ones is called variable

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selection.

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In the coming videos, we learn some important methods.

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Let's give us these two benefits.

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We will be discussing two types of methods primarily.

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One type is called subsects election.

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In this method, we use a subset of B variables in our model.

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Instead of using all the variables.

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The second type of method is called shrinkage methods.

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In these type of method, we try to string Dondi coalitions of variables to zero.

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This is also known as regularisation.

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So in the coming videos, we will look at alternative models, which may increase model accuracy and

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interpretively.