# Learning Curves

- Curve to check the learning algorithm, whether bias, variance, or both

- To plot learning curve, take small subset of training set(10-40 training examples)
- training set for m = 3, training set = 0. slightly larger if using regularization
- Learning curves will only make a shape only based on that three training example
- Say m >= 4, will become harder for the curve to shape all training set perfectly
- Concretely more training examples, the harder the curve to fit all the examples.
- The more data we have, the more fit the cross validation error, that is better hyphothesis for more training examples
- Sooo, with m start small, the error of Jtrain will be small, because it will overfit smaller data

- This is what we get for the curve if what we have is either bias or variance
- First choosing the model (See hypothesis formula) that is only the linear model.
- The high bias can be shown as the three Jtest, Jtrain, and Jcv are all end up at high value
- suffering High Bias can't really help much if we get more training data.
- As we taught earlier, the Jcv will be similar to Jtrain if the learning suffer high bias, the error will be usually high.

- Next is if we suffering high variance, denotes by Jc) >>(much higher) Jtrain.
- Suppose we use hypothesis with a lot of high order polynomials and small lambda
- Jtrain if get more data, will generally close to future example,
- But more the data the cross validation will keep decreasing(less error), and the Jtrain will keep increasing(more error)
- Sometimes curve can be a lot cleaner/messier, but still helps out intuition if it has bias/variance problem

- Next, discussed about spesific actions to take (or not take) to increase the performance of learning algorithm

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