Evaluating a hyphotesis

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Evaluating a hyphotesis
  • Choosing the correct parameters whether is underfitting or overfitting


  • Much harder evaluate hypothesis if we have much more features
  • Split training examples, for first the usual training set.
  • The second, is the test set.
  • 70% for training set and 30% set for test set for the split term
  • Recommended for the training set to be shuffled, or random sorted



  • Inject the theta from J theta training set and implement it for theta in Jerror test
  • These are the test error for linear regression
  • How about logistic regression at classification?
  • The process is similar
  • Takes theta from 70% examples, that is training set, then plug in the theta for J error
  • Sometimes there's alternative, that is missclassfication method that simpler
  • If the hyphothesis is inccorrect predicting the value of output, then label 0 to hyphotesis, 1 otherwise

  • These are few examples for evaluating hyphotesis
  • Next, Choose what features, or degree of plynomials, or choosing regularization for learning algorithm