Classification And Overfitting

This is a learning note of Logistic Regression of Machine Learning by Andrew Ng on Coursera. Hypothesis Representation Uses the "Sigmoid Function," also called the "Logistic Function": Which turn linear regression into classification. Sigmoid function looks like this: give us the probability that the output is 1. In fact, is simplified as for logistic regression, and is for linear regression. In some complicated case, z might be something like: Decision Boundary Decision boundary is the line (or hyperplane) that separates the area where y = 0 and where y = 1 (or separates different classes). It's created by our hypothesis function. The input to the sigmoid function is not necessary to be linear, and could be a function that describes a circle (e.g. ) or any shape to fit the data. Cost Function Using the cost function for linear regression in classification will cause the output to be wavy, resulting in many local optima.