In this video, I will discuss with you the results of our models so that when you see the results,
you are able to a call business insight for your business problem.
If you remember when we landed logistic model, we get a desert like this in our model, which contains
all the beta values.
And this column, DEPI values in this column for all the independent variables.
So first thing that we should look at when we have this table is this column containing the P value.
As I told you earlier, also P value represents our confidence level when you are saying that whether
this variable is impacting the response variable or not.
So this variable residential area has a P value, which is very small.
This ease of presenting exponential, so to about minus 13.
So this value is very small.
Which means that we are very confident that this variable is impacting our response variable.
Similarly, all these values, which are very small, are impacting our response very well.
If you look at air quality, it is zero point zero two seven, which is less than five percent.
We can still take air quality and in hospitals, all these other variables, which are more than zero
point zero five for these variables.
We are not sufficiently confident to make these Stegeman that these variables are actually impacting
our response variable.
So if you want to remove some of the variables, we will pick the variable with largest P value and
we can remove it.
So the first thing we see is this column and identify the variables would be value less than zero point
zero five.
Next thing we do is to look at this bit of values.
If you remember, Bieda, values represent the impact of this variable, these individual variables
on the response variable.
So if this beta values large, this variable will have a large impact.
If this value small, this variable will have a smaller impact on the response.
Very good.
Second thing is the importance of the sign.
If this sign is negative, it means that if I increase price, the chance of house getting sold decreases.
So this sign, the present inverse relationship.
If I increase this variable, the response variable will increase because it is having a positive sign.
This is having negative sign.
So if I increase equality index, the chance of getting sold will decrease.
So two things to note.
Then we have beta.
First is how large bidets and taking this or this design of BREEDA.
So this is how we interpret the coefficient table.
Similarly, in linear, indiscriminate axis, we get linear discriminant coefficients.
Those also mean the same thing.
We again see the value of the coefficient and the signs of the coefficient.
However, this is not available in Ganon.
As I told you earlier, kanon is non parametric.
There is no functional relationship.
So there are nobody does.
Therefore, it is very difficult to estimate the effect of individual productivity was on the response
variable.
When we are doing Kiernan.
So that is one of the drawbacks of Giana that it does not tell us anything about the relationship of
each variable with the response variable.
Second thing I want to discuss is the comparison of all these three classifiers in terms of accuracy
for our dataset.
We got lda with the highest accuracy, but in general, we cannot say that A will always perform the
best.
Whenever a linear boundary best classify is the dataset, logistic and Eilidh both perform well.
Whereas whenever there is a nonlinear boundary, gaining performs better than these lenient methods
out of logistic.
And Ailee, since in LDA we had an assumption that the continuous variables are normally distributed.
If that assumption is make a live performance better.
If that assumption is wrong, logistic regression performs better.
You can see these are the three confusion metrics.
This is not the conclusion metrics.
We got a particular dataset.
This confusion metrics is drawn only on the test.
We train the model on one data and tested it on another.
If we would have drawn the convergent metrics on the training set, only probably Keinon would be the
best method coming out.
However, when we do it on a desert, Ali is the best method.
So this accuracy is just the correct predictions, which is 70 out of the total observations, which
was one two indeed.
So this is surrounded by 120.
This is 80 by 120.
And this is sixty six by one grindy.
So whenever we had logistic Aley, you can get the individual impact of all the variables on the response
variable.
When we are comparing the performance of three different methods, we will draw the conclusion matrix
of all these three using a separate data called the test data, and then really compare their accuracy
to find out which of these classifier is performing the best for our data.