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# Cost Function

We can measure the **accuracy** of our hypothesis function by using a cost function. This takes an average (actually a fancier version of an average) of all the results of the hypothesis with inputs from x's compared to the actual output y's.

If m is the number of training examples, the cost function for *Linear Regression in One Variable* is given by:

$$J(θ\_0,θ\_1) = (1/2m) ∑\_{i=1}^{m}(h\_θ(x^{(i)})−y^{(i)})^2$$

Lower values indicate more accuracy.

This function is otherwise called the "Squared error function", or Mean squared error.

We can plot it on a graph taking $$θ\_0$$ and $$θ\_1$$ on the x and z axis respectively, and J on the y axis:

![](/files/-M5-0U6bc4M50NCGyiY6)


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