1 minute read


喔喔喔~~~ 明天第一天上課 好期待 :)

Deciding What to Try Next

  • When you test your hypothesis on a new set of houses, you find that it makes unacceptably large errors in its predictions.
    What should you try next?

    • Get more training examples

    • Try smaller sets of features

    • Try getting additional features

    • Try adding polynomial featyres

    • Try decreasing λ

    • Try increasing λ

  • Machine learning diagnostic:

    • Diagnostic: A test that you can run to gain insight what is/isn’t working with a learning algorithm, and gain guidance as to how best to improve its performance.

    • Diagnostics can take time to implement, but doing so can be very good use of your time.

    • A diagnostic can sometimes rule out certain courses of action (changes to your learning algorithm) as being unlikely to improve its performance significantly

Evaluating a Hypothesis : article

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

    • Getting more training examples

    • Trying smaller sets of features

    • Trying addiontal features

    • Trying polynomial features

    • Increasing or decreasing λ

  • 把資料集合分成 training settest set,通常比例分成 70% 和 30 %

    • The new procedure using these two sets is then:

      1. Learn Θ and minimize Jtrain(Θ) using the training set

      2. Compute the test set error Jtest(Θ)

The test set error

  1. For linear regression Imgur
  2. For Classification ~ Misclassification error (aka 0/1 misclassification error): Imgur

Model Selection and Train/Validation/Test Sets : article

Model Selection:

用 test set 來去抓J(θ),結果會造成 Imgur

Train / Validation / Test Sets Imgur

Train / Validation / Test Sets Error Imgur