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And so let's actually use the model.
So, next we've now trained a liner regression model, let's evaluate it.
So, what we're gonna do next is
#Evaluate the simple model.
So how to evaluate it?
We're gonna look at the test data.
So, remember we had the split test data.
Let's understand test data a little bit.
So, for example, let's print.
For the test data, for
the price column, what's the average price?
What's the mean price?
So this just computes the average price and the average price for
the test data, for this data from Seattle is $543,000.
That's how average house costs, it's pretty expensive actually.
Now, we've built a square foot model, and so
what we want to do is evaluate it on this test data.
So we're going to take the sqft_model
that we built and we're gonna call what's called evaluate function.
Which can take a test data set and print out or
return some statistics of how well that fit is doing.
So let's do that.
So, I'm actually going to type, print in the beginning,
because it formats it a little bit nicer.
And you'll see that the maximal error over all test houses was 4.1 million.
So there was one house that was an outlier, it was really badly predicted.
And average error, so the RMSE, so the root means squared error,
we talked about this.
Emily talked about this with us during the lectures,
is $255,000, so that's the RMSE.
So we built this simple model, we tested, has pretty high RMSE,
but let's look at some predictions it tries to make from the data.
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