Let's continue evaluating our model.
We learned about MSE and E and we plotted in this diagram.
Now let's create a new cell and evaluate on test set.
Start by test.
Underscore loss test may test MSE.
Model evaluate test X test.
Scaled y test and set the verbose equals to zero.
Verbose equals to zero.
Let's test the loss.
Test loss get four digits after the point you can.
After the decimal point, you can make it two for better performance.
Let me run and here we go.
Test loss 6.45.
Test 1.8 and test Ma MSE equals 6.45.
Let's analyze those numbers and results.
Let's start with the test loss.
The test loss is 6.45, same as MSE since you used MSE as loss function.
So we can interpret only the MSE and so we can remove it.
Let's run again and get the two parameters two values and evaluation metrics may and MSE m 1.8.
On average your predictions are plus or -1.8 units away from the actual values.
So this is a very important note I want from you to write this note down because it's um, it's very
important and it helps you understand why we use those metrics for future predictions.
So may your predictions are plus or -1.8 units away from the actual values.
This is your most interpretable metric M 6.45.
The average squared error is 6.45.
Since MC squared errors, it's more sensitive to large errors.
But we are going to depend on this Ma.
The model is learning but has room for improvement.
Ma 1.8 is significantly lower than MSE, suggesting most errors are small with few large outliers.
The model has learned the general pattern of your data, so this is the conclusion.
Whenever you get Ma is less than MSE and you get lower values of Ma, you model is being learned for
the general pattern of your data.
If your target values range from 0 to 10 Ma equals to one point A is descent.
If your target values range from 0 to 100.
The Ma 1.8 is excellent.
So it's good in our case because we are arranging the data and the target values are between 0 and 100.
So we are good.
So depending on what are you predicting.
House prices temperatures mileage.
What's the typical range of your target variable.
The what's the context of your problem.
This will help determine if your current performance is acceptable or needs significant improvement.
In our case, it's good and there's no need for better improvement.