machine learning(13) -- solving the problem of overfitting:regularization
solving the problem of overfitting:regularization
- 发生的在linear regression上面的overfitting问题
- 发生在logistic regression上面的overfitting
- 怎么解决overfitting
- regularization: cost function of linear regression
- parameters小的话,这样hypothesis就会变得简单,这样就不会overfitting
- 一般不会对θ0进行regularization
- 上式是进行regularization的linear regression的cost function,要使上式的值取最小值
- 对这个cost function 的分析
- 由两个式子(两个目标)组成,第一个式子是为了对trainning data更好的拟合(fitting the training data),第二个式子是为了避免overfitting
- 第二个式子叫regularization term, λ叫regularization parameter, λ是为了平衡两个目标用的
- 如果 λ非常大的话(这时θ1-θn几乎为0,hypothesis变得很简单,只有常数),就会出现underfitting,对trainning data/ new data很低的fitting
- 所以并不是regularization在任何情况下(当 λ非常大的情况下),都能使model更适应new data或者training data
- The regularization term puts a penalty on the cost J,随着模型参数的增多,the penalty increases as well.
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