Once we have done some trouble shooting for errors in our predictions by:
Getting more training examples
Trying smaller sets of features
Trying additional features
Trying polynomial features
Increasing or decreasing λ
We can move on to evaluate our new hypothesis.
A hypothesis may have a low error for the training examples but still be inaccurate (because of overfitting). Thus, to evaluate a hypothesis, given a dataset of training examples, we can split up the data into two sets: atraining setand atest set. Typically, the training set consists of 70 % of your data and the test set is the remaining 30 %.
The new procedure using these two sets is then:
1. Learn??and minimize
using the training set
2. Compute the test set error?
The test set error
1. For linear regression:??
2. For classification ~ Misclassification error (aka 0/1 misclassification error):
This gives us a binary 0 or 1 error result based on a misclassification. The average test error for the test set is:
This gives us the proportion of the test data that was misclassified.
來(lái)源：coursera 斯坦福 吳恩達(dá) 機(jī)器學(xué)習(xí)
