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

  • high bias (underfit)
    • tendency to constantly learn the same wrong thing
    • you're always missing in the same way
  • high variance (Overfitting)
    • tendency to output random things irrespective to the input data
    • you depend too much on the training data

Dart throwing illustration:

  • high-variance-bias.png

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 $J_{\text{cv}}(\theta)$


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:

  • 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


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