Cost Function
The measurement of accuracy of a hypothesis function. The accuracy is given as an average difference of all the results of the hypothesis from the inputs (x’s) to the outputs (y’s). J(Θ0,Θ1)=12mm∑i=1(hΘ(xi)−yi)2
where m is the number of inputs (e.g. training examples)
This function is also known as the squared error function or mean squared error. The 12 is a convenience for the cancellation of the 2 which will be present due to the squared term being derived (see gradient descent).
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