Suppose you have implemented regularized logistic regression to classify what object is in an image (i.e., to do object recognition). However, when you test your hypothesis on a new set of images, you find that it makes unacceptably large errors with its predictions on the new images. However, your hypothesis performs well (has low error) on the training set. Which of the following are promising steps to take? Check all that apply.
A.Try evaluating the hypothesis on a cross validation set rather than the test set.
B.(CHECKED) Try increasing the regularization parameter λ.
C.Try decreasing the regularization parameter λ.
D.(CHECKED) Try using a smaller set of features.
总结：
高方差：(训练误差小，测试误差大）
增大训练样本
减少特征
增大λ
高偏差：（训练误差大，测试误差大，且两者相近）
添加特征
增加阶数
减小λ
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