机器学习（六） — 评估模型

,b)=−mtest​1​i=1∑mtest​​[ytest(i)​log(f(xtest(i)​))+(1−ytest(i)​)log(1−f(xtest(i)​)]

2 cross-validation set

1. split the training set into training set, cross-validation set and test set
2. the cross-validation set is used to automatically choose the better model, and the test set is used to evaluate the model that chosed

3 bias and variance

1. high bias: $J_{train}$
2. high variance: $J_{train}$

3. if high bias: get more training set is helpless
4. if high variance: get more training set is helpful

4 regularization

1. if $\lambda$
2. if $\lambda$

5 method

1. fix high variance:
   • get more training set
   • try smaller set of features
   • reduce some of the higher-order terms
   • increase $\lambda$
2. fix high bias:
   • get more addtional features
   • add polynomial features
   • decrease $\lambda$

6 neural network and bias variance

1. a bigger network means a more complex model, so it will solve the high bias
2. more data is helpful to solve high variance

3. it turns out that a bigger(may be overfitting) and well regularized neural network is better than a small neural network

代码007普通

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