Linear Regression cost function:$$J(\theta) = \frac{1}{2 m} \sum_{i=1}^m (h_{\theta}(x^{(i)}) - y^{(i)})^2$$where:$$h_{\theta}(x) = \theta_0 + \theta_1 x_1$$Logistic Regression cost function$$J(\theta) = \frac{1}{m} \sum_{i=1}^m - (y^{(i)} \times log(h_{\theta}(x{(i)})) + (1-y{(i)}) \times log(1 - h_{\theta}(x{(i)})))$$where:$$h_{\theta}(x) = g(\theta_0 + \theta_1 x_1 + \theta_2 x_2)$$Intuitively linear regression is easy to understand as it optimizes the average squared distance between hypothesis and training data. But in case of logistic regression, I failed to understand the cost function. What does the logistic regression cost function represent?
Understanding Logistic Regression Cost function
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