美文网首页
【机器学习】-Week6.1 Evaluating a Hypo

【机器学习】-Week6.1 Evaluating a Hypo

作者: Kitty_风花 | 来源:发表于2019-12-29 09:58 被阅读0次

    Once we have done some trouble shooting for errors in our predictions by:

    \bullet Getting more training examples

    \bullet Trying smaller sets of features

    \bullet Trying additional features

    \bullet Trying polynomial features

    \bullet 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 \theta  and minimize J_{train}(\theta ) using the training set

    2. Compute the test set error J_{test}(\theta )

    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.

    来源:coursera 斯坦福 吴恩达 机器学习

    相关文章

      网友评论

          本文标题:【机器学习】-Week6.1 Evaluating a Hypo

          本文链接:https://www.haomeiwen.com/subject/ttlqmctx.html