Multiclass Classification: One-vs-all

Classify more than just 0 and 1. Examples:

Summary by Filip:

Logistic regression is a classification algorithm and not a regression algorithm, although "regression" is part of the name
Hypothesis equals sigmoid/logistic function of theta_transpose * x
The derivative of J(theta) is the same as for linear regression, i.e. 1/m * sum((h(x) - y)*x). However, the hypothesis is different. While the hypothesis in linear regression is h(x) = theta_transpose * X , the hypothesis in logistic regression is 1/(1+e^(theta_transpose * x))
Function z does not have to be linear; it can be everything, e.g. circle
When dealing with multiclass logistic regression, if we follow the one vs. all approach, we can simplify the problem to simple binary classification problem. So we estimate P(y=i|x;theta) for i={1,..n} for each class and take the max

Resources:

Machine Learning