In the last few lectures, we saw how to predict a binary response using a single predicted.
Now let us see how to handle multiple predictors.
So mathematically, the danger is very small.
Earlier, we had only B does, you know, plus B, the one X.
Now it will include all the B predictors that we are going to use and will have correspondingly B plus
one.
B does.
So we predicted.
With this small change, we will grindy model again.
This is maximum likelihood criteria and we will get the values of all these redoes using the beta values,
we can get the probability value for any set of observations.
And if the probability is greater than the boundary limit, which we can set as but our own convenience
for our house placing it, it will be using a body limit of zero point five.
So if the program is coming out to be more than point crape, we will see that the house will be sold
within three months.
If it is coming less than point, we will say that it will not be sold within three months.
So this is how we handle multiple predictors.
Anything I want to talk about here is can we handle multiple classes in the response variable?
So right now our dataset has only two classes and the response variable, that is whether it will be
sold within three months or not.
If our response variable had three classes, say, whether it will be sold within three months, within
a year or more than a year.
Can that be done?
Answer is yes.
Logistic regression can be applied for multi class response variable, and it can be done in most software
packages like art and Python easily.
But in practice, we do not use logistic regression in such a scenario.
One of the popular techniques in such a scenario is linear discriminant analysis.
So let us first train the model with binary classes and all the predictors we have in our dataset.
Then I will tell you about linear discriminant analysis in the coming few lectures.