source: from machine-learning on Coursera by Dr. Andrew Ng
in category: Machine Learning_tricks4better performance
(see this post for a deeper intro to bias and variances and talk about how it interacts with and is affected by the regularization of your learning algorithm.)
If you run the learning algorithm and it doesn’t do as well as you are hoping, almost all the time it will be because you have either a high bias problem or a high variance problem. In other words they’re either an underfitting problem or an overfitting problem.
And in this case it’s very important to figure out which of these two problems is bias or variance or a bit of both that you actually have. Because knowing which of these two things is happening would give a very strong indicator for whether the useful and promising ways to try to improve your algorithm.
