Cost Function

Machine-Learning Linear-Regression

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). \begin{equation} J(\Theta_{0},\Theta_{1})=\frac{1}{2m}\sum_{i=1}^{m}(h_{\Theta}(x_{i}) - y_{i})^{2} \end{equation} 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 \(\frac{1}{2}\) is a convenience for the cancellation of the 2 which will be present due to the squared term being derived (see gradient descent). ...