So now you have created a where we base our model.
We have predicted why best and wide-screen values using that model.
Now it's time to evaluate the performance of our model using these projected values.
There are two important metrics which we use to evaluate our model performance.
The first one is mean squared error.
This is the mean of is square off or a deviation of predicted values from the actual values.
The second parameter is R-squared value.
This is the goodness of fit for the modern artist square.
Lies between zero and one one sense for perfect fit.
That means we are able to justify all the variations in our Y variable using our model.
And zero means no fake.
We are not able to identify any deviations and by using our model generally are the square lies between
zero point four two zero point eight.
For good models and zero point eight annable for excellent models.
There is no such range for mean squared error.
This is an absolute number.
You cannot compare it across different projects.
This depends on your current dataset and you can use MASC to evaluate performance of different models
on the same dataset.
Now it is very easy to calculate.
This is escort's using a skillern.
First you have to import mean a squared error and are to underscore the score from Escalon.
Metrics will just run this.
Come on.
Now to calculate the MASC, you just separate mean under score squared under score error.
Then you have to give your actual Y values and you are credited y values.
We have already predicted our Y values in our last lecture, so we will use just y under score tests
and by underscore tests.
Underscore pride as our arguments.
Zukin can see the mean squared error on our test dataset is one hundred and eleven million.
We can only use mini skirt edit to calculate performance of different models on our similar task.
So right now this has no meaning for us.
Let's move on to artist square value for our treene dataset.
We have the predicted values.
Are Wyandotte score green on that score, pride and the actual user in the score screen.
So our artist square value is point A three.
This means our model is performing great.
Now let's calculate.
Artists could well lose on our test data.
So remember, we have created our model on this train data and we cannot use this train data to evaluate
the performance.
We have to give the test data that we have kept aside while creating our model.
The artist square value for our test dataset is zero point six five.
You should always look at the best artists good values who really would do more than performance, since
you have not used this as their tough during model training.
And we are getting artists to value more for our train, that asset as cumber bellard as there does
it, because it is obvious we have trained our model on our train does it and it will always perform
better on the dataset on which it has trained.
So it is obvious to get artists to value great for our crane dataset then our estimates say.