MLDIAGNOSTICS
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The document provides advice and guidance for applying machine learning algorithms. It discusses debugging a learning algorithm that is making large errors by trying different techniques such as getting more training data, changing the features, decreasing or increasing the learning rate, etc. It also covers evaluating hypotheses on test data, diagnosing bias vs variance issues, using regularization to address overfitting, and analyzing learning curves.
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MLDIAGNOSTICS
- 1. Advice for applying machine learning Deciding what to try next Machine Learning
- 2. Debugging a learning algorithm: Suppose you have implemented regularized linear regression to predict housing prices. However, when you test your hypothesis on a new set of houses, you find that it makes unacceptably large errors in its predicDons. What should you try next? -‐ -‐ -‐ -‐ -‐ -‐ Get more training examples Try smaller sets of features Try geJng addiDonal features Try adding polynomial features Try decreasing Try increasing Andrew Ng
- 3. Machine learning diagnos5c: DiagnosDc: 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. DiagnosDcs can take Dme to implement, but doing so can be a very good use of your Dme. Andrew Ng
- 4. Advice for applying machine learning EvaluaDng a hypothesis Machine Learning
- 5. Evalua5ng your hypothesis price Fails to generalize to new examples not in training set. size size of house no. of bedrooms no. of floors age of house average income in neighborhood kitchen size Andrew Ng
- 6. Evalua5ng your hypothesis Dataset: Size Price 2104 1600 2400 1416 3000 1985 1534 1427 1380 1494 400 330 369 232 540 300 315 199 212 243 Andrew Ng
- 7. Training/tes5ng procedure for linear regression -‐ Learn parameter from training data (minimizing training error ) -‐ Compute test set error: Andrew Ng
- 8. Training/tes5ng procedure for logis5c regression -‐ Learn parameter from training data -‐ Compute test set error: -‐ MisclassificaDon error (0/1 misclassificaDon error): Andrew Ng
- 9. Advice for applying machine learning Model selecDon and training/validaDon/test sets Machine Learning
- 10. price OverfiBng example size Once parameters were fit to some set of data (training set), the error of the parameters as measured on that data (the training error xxxxx) is likely to be lower than the actual generalizaDon error. Andrew Ng
- 11. Model selec5on 1. 2. 3. 10. Choose How well does the model generalize? Report test set error . Problem: is likely to be an opDmisDc esDmate of generalizaDon error. I.e. our extra parameter ( = degree of polynomial) is fit to test set. Andrew Ng
- 12. Evalua5ng your hypothesis Dataset: Size Price 2104 1600 2400 1416 3000 1985 1534 1427 1380 1494 400 330 369 232 540 300 315 199 212 243 Andrew Ng
- 13. Train/valida5on/test error Training error: Cross ValidaDon error: Test error: Andrew Ng
- 14. Model selec5on 1. 2. 3. 10. Pick EsDmate generalizaDon error for test set Andrew Ng
- 15. Advice for applying machine learning Diagnosing bias vs. variance Machine Learning
- 16. Size High bias (underfit) Price Price Price Bias/variance Size “Just right” Size High variance (overfit) Andrew Ng
- 17. Bias/variance Training error: error Cross validaDon error: degree of polynomial d Andrew Ng
- 18. Diagnosing bias vs. variance error Suppose your learning algorithm is performing less well than you were hoping. ( or is high.) Is it a bias problem or a variance problem? Bias (underfit): (cross validaDon error) Variance (overfit): (training error) degree of polynomial d Andrew Ng
- 19. Advice for applying machine learning RegularizaDon and bias/variance Machine Learning
- 20. Linear regression with regulariza5on Price Price Price Model: Size Size Large xx High bias (underfit) Intermediate xx “Just right” Size Small xx High variance (overfit) Andrew Ng
- 21. Choosing the regulariza5on parameter Andrew Ng
- 22. Choosing the regulariza5on parameter Model: 1. Try 2. Try 3. Try 4. Try 5. Try 12. Try Pick (say) . Test error: Andrew Ng
- 23. Bias/variance as a func5on of the regulariza5on parameter Andrew Ng
- 24. Advice for applying machine learning Learning curves Machine Learning
- 25. error Learning curves (training set size) Andrew Ng
- 26. error price High bias (training set size) If a learning algorithm is suffering from high bias, geJng more training data will not (by itself) help much. price size size Andrew Ng
- 27. High variance error price (and small ) (training set size) If a learning algorithm is suffering from high variance, geJng more training data is likely to help. price size size Andrew Ng
- 28. Advice for applying machine learning Deciding what to try next (revisited) Machine Learning
- 29. Debugging a learning algorithm: Suppose you have implemented regularized linear regression to predict housing prices. However, when you test your hypothesis in a new set of houses, you find that it makes unacceptably large errors in its predicDon. What should you try next? -‐ -‐ -‐ -‐ -‐ -‐ Get more training examples Try smaller sets of features Try geJng addiDonal features Try adding polynomial features Try decreasing Try increasing Andrew Ng
- 30. Neural networks and overfiBng “Small” neural network (fewer parameters; more prone to underfiJng) ComputaDonally cheaper “Large” neural network (more parameters; more prone to overfiJng) ComputaDonally more expensive. Use regularizaDon ( ) to address overfiJng. Andrew Ng