This is where the concept of ordinary lease squar es regression comes in. And the idea is very simple.

So again, the values of the coefficients are estimated such that the model comes up with optimal predictions.

Here we start implementing the delta debugging function.

So, we wanna minimize the difference between the observed scores on Y and predicted scores on Y, predicted by the model.

It goes from two forty down to two hundred.

And that would give us the sum of squared residuals.

When we fit the parameters of our learning algorithm we think about choosing the parameters to minimize the training error.

Look for smaller changes

And to make some of our, make the math a little bit easier, I'm going to actually look at, you know, 1 over M times that.

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However, in practice you're often just given the training data error.

It's already zero for the original setting.

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Not surprisingly the best complexity is obtained where the generalization error is minimum.

The idea is to use gradient descent.

It doesn't change it quite enough, but that protein acts just a little bit different.

Let's multiply both sides of this equation by dx.

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