• Above are examples of many algorithms that can include very complete function/ incorporate many features. Then that means the algorithm has low bias.
  • And given lots of data, the learning algorithm is likely less overfit
  • Incorporate these both method we can have low bias and low variance.
  • And if Jtrain is low, and Jtrain approximately equal to Jtest, then we will have high performance learning algorithm.
  • Checkbox 2 and 4 is wrong. Because it is the cases where we have overfit problem. so increasing training set(very large) will be a huge help.
  • Checkbox 1 and 3 is correct. Because we don' have enough features, even large training set will not give enough help to increase learning algorithm to predict the correct value

  • In summary , having lots of data, and lots of features will give us a powerful learning algorithm.
  • Key test to observe:
    • Try with human expert to predict the output value (y value). Try to see if the feature given are possible for human to solve.
    • Next, if we have lots of parameters and lots of training sets, then we will have significantly better learning algorithm