Okay, so our quality metric is gonna take our predictions and
our actual house sales observations, spit out this error, and
it's gonna go into a machine learning algorithm that's gonna be used to update
the weights, update our parameters of our model.
And we're gonna talk about this machine learning algorithm, or
different variants of it, a lot more in the actual course on regression.
But this is the overall flowchart for this machine learning method for
our house prediction problem.
And this loop here, where we're taking our predictions,
computing our error relative to the actual house sales prices,
and updating the weights or our model parameters.
This process tends to happen in an iterative way,
where we update values again and again.
Okay, so if we abstract a way, what we see is we have some training data set.
We have some feature extraction process.
We have some machine learning model.
And it's gonna produce some intelligence, which in this case is a prediction.
And we're gonna assess the quality of our intelligence with some quality measure.
And we're gonna use that error or accuracy, depending which way we're
thinking about measuring it, to adjust our model parameters using some algorithm.
And we're gonna see this type of flow for machine learning again and again.