If the squared error of the line is really small what does that mean?

直线的平方误真的很小，那意味着什么？

In the cost function, the squared error for the second example is also 0 squared.

在成本函数中，第二个示例的平方误也是 0 平方。

So are squared error versus our line, our total squared error, we just computed to be 2.74.

所以我们的平方误相对于这条线，总的平方误我们刚刚计算出来是2.74。

If the squared error of the line is small, it tells us that the line is a good fit.

直线的平方误很小，就说明这条直线拟合得很好。

And so what we calculated next was the total error, the squared error, from the means of our y values.

然后我们计算的是总误，即y值平均数的平方误。

So that it minimizes the squared error.

从而最小化平方误。

And what we want to do is minimize this squared error from each of these points to the line.

我们要做的是最小化这些点到直线的平方误。

So this is the squared error of the line.

所以这就是这条直线的平方误。

And so if the squared error of the line is large, this whole thing's going to be close to 1.

这条线的平方误很大，那么整个式子就会接近1。

And then we're going to square that error.

然后我们将对那个误进行平方。

This is the squared error of the line axis.

这是直线轴的平方误。

You put it into this expression for the squared error of the line.

你把它代入这条线的平方误表达式中。

If the squared error of the line is huge, then that means there's a lot of error between the data points and the line.

直线的平方误很大，那就意味着数据点与直线之间的误很多。

So if we find the m and the b values that satisfy the system of equations, we have minimized the squared error.

所以我们找到了满足方程组的m和b值，我们就最小化了平方误。

So once again, this is just the squared error of that line with n points.

所以再一次，这只是那条有 n 个点的直线的平方误。

Our goal is to find the m and the b, which would define an actual line, that minimize the squared error.

我们的目标是找到斜率m和截距b，它们定义了一条实际的直线， 使平方误最小。

We have the squared error of the line.

我们有了这条线的平方误。

And we're taking the sum of the squared error between each of those n points and our actual line, y equals mx plus b.

我们将每个n点与实际直线y=mx+b之间的平方误求和。

Let me call this the squared error from the average.

让我称这个为平均值的平方误。

And so you can even visualize it the same way we visualized the squared error from the line.

你可以用同样的方式来可视化它，就像我们可视化从回归线的平方误一样。