Machine Learning Diagnosis

Suppose you created a model, but when you tested it, you found that it makes large errors

What should you try?

  • Get more training examples
  • Try smaller set of features
  • Try getting additional features
  • Try adding polynomial features (beware of Overfitting!)
  • Try increasing regularization parameter $\lambda$
  • Try decreasing $\lambda $


Diagnosis - a test that you can run to gain insights what is working with the learning algorithms and what is not, and gain guidance as how to improve the performance.


Evaluating a Hypothesis

To test if we overfit, we can perform Cross-Validation:

  • train the model on the training set
  • check the model on the test set


Diagnosing Bias vs Variance

the main sources of problems are


Fitting Polynomial

How to distinguish between them and say which one of them we experience?

  • Suppose we want to fit parameter $d$ - what degree of polynomial to use (see here)
  • with $d = 1$ we underfit
  • with $d = 2$ we are just right
  • with $d = 4$ we overfit


We can plot the cost function errors vs degree of polynomial $d$ for

  • the training set $J_{\text{train}}(\theta)$
  • the cross-validation (or test) set $J_{\text{cv}}(\theta)$

diagnosis-bias-variance.png

in case of bias (underfit) we have

  • both $J_{\text{train}}(\theta)$ and $J_{\text{cv}}(\theta)$ are high
  • and $J_{\text{train}}(\theta) \approx J_{\text{cv}}(\theta)$

in case of variance (overfit)

  • $J_{\text{train}}(\theta)$ is low, $but J_{\text{cv}}(\theta)$ is high
  • and $J_{\text{cv}}(\theta) \gg J_{\text{train}}(\theta)$ (much greater)


Fitting Regularization Parameter

When we try to find the best Regularization parameter for a hypothesis we get similar curves:

diagnosis-regularization-curve.png
  • with small $\lambda$ we have high variance
  • with large $\lambda$ we have high bias


Learning Curves

Learning Curves is a technique that is used to


What To Do Next?

So, depending on what kind of problem we have, we should decide what to do next

To fix high variance:

To fix high bias:


See also


Sources

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