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Okay, so let's summarize what we've learned in this module.
And these notions that we've learned here are really,
really important to machine learning.
Well beyond just this regression task.
So this was a really important module on how to assess performance
that we're gonna see again and again.
Throughout this machine learning specialization.
In particular, we described what a loss function is and
we gave different examples of loss functions.
We also talked about differentiating between notions of training error,
true and generalization error, and test error.
And we talked about how to think about splitting our data into training and
test sets.
And in order to both fit our model parameters as well as to estimate
our generalization error.
And we also talked about how to think about
three notions of error that come into our predictions.
And finally, we talked about this idea of
choosing the model complexity as a key component of our work flow.
But how to think about doing that by introducing this notion
of a validation set.
And using this to allow us to choose model complexity and
then to assess our performance on a separate test set.
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