Week 7

07: Regularization

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The problem of overfitting

  • So far we've seen a few algorithms - work well for many applications, but can suffer from the problem of overfitting
  • What is overfitting?
  • What is regularization and how does it help

Overfitting with linear regression

  • Using our house pricing example again
    • Fit a linear function to the data - not a great model
      • This is underfitting - also known as high bias
      • Bias is a historic/technical one - if we're fitting a straight line to the data we have a strong preconception that there should be a linear fit
        • In this case, this is not correct, but a straight line can't help being straight!
    • Fit a quadratic function
      • Works well
    • Fit a 4th order polynomial
      • Now curve fit's through all five examples
        • Seems to do a good job fitting the training set
        • But, despite fitting the data we've provided very well, this is actually not such a good model
      • This is overfitting - also known as high variance
    • Algorithm has high variance
      • High variance - if fitting high order polynomial then the hypothesis can basically fit any data
      • Space of hypothesis is too large



  • To recap, if we have too many features then the learned hypothesis may give a cost function of exactly zero
    • But this tries too hard to fit the training set
    • Fails to provide a general solution - unable to generalize (apply to new examples)

Overfitting with logistic regression

  • Same thing can happen to logistic regression
    • Sigmoidal function is an underfit
    • But a high order polynomial gives and overfitting (high variance hypothesis)



Addressing overfitting

  • Later we'll look at identifying when overfitting and underfitting is occurring
  • Earlier we just plotted a higher order function - saw that it looks "too curvy"
    • Plotting hypothesis is one way to decide, but doesn't always work
    • Often have lots of a features - here it's not just a case of selecting a degree polynomial, but also harder to plot the data and visualize to decide what features to keep and which to drop
    • If you have lots of features and little data - overfitting can be a problem
  • How do we deal with this?
    • 1) Reduce number of features
      • Manually select which features to keep
      • Model selection algorithms are discussed later (good for reducing number of features)
      • But, in reducing the number of features we lose some information
        • Ideally select those features which minimize data loss, but even so, some info is lost
    • 2) Regularization
      • Keep all features, but reduce magnitude of parameters θ
      • Works well when we have a lot of features, each of which contributes a bit to predicting y


Cost function optimization for regularization

  • Penalize and make some of the θ parameters really small
    • e.g. here θ3 and θ4


  • The addition in blue is a modification of our cost function to help penalize θ3 and θ4
    • So here we end up with θ3 and θ4 being close to zero (because the constants are massive)
    • So we're basically left with a quadratic function


  • In this example, we penalized two of the parameter values
    • More generally, regularization is as follows
  • Regularization
    • Small values for parameters corresponds to a simpler hypothesis (you effectively get rid of some of the terms)
    • A simpler hypothesis is less prone to overfitting
  • Another example
    • Have 100 features x1, x2, ..., x100
    • Unlike the polynomial example, we don't know what are the high order terms
      • How do we pick the ones to pick to shrink?
    • With regularization, take cost function and modify it to shrink all the parameters
      • Add a term at the end
        • This regularization term shrinks every parameter
        • By convention you don't penalize θ0 - minimization is from θ1 onwards


  • In practice, if you include θ0 has little impact
  • λ is the regularization parameter
    • Controls a trade off between our two goals
      • 1) Want to fit the training set well
      • 2) Want to keep parameters small
  • With our example, using the regularized objective (i.e. the cost function with the regularization term) you get a much smoother curve which fits the data and gives a much better hypothesis
    • If λ is very large we end up penalizing ALL the parameters (θ1, θ2 etc.) so all the parameters end up being close to zero
      • If this happens, it's like we got rid of all the terms in the hypothesis
        • This results here is then under fitting
      • So this hypothesis is too biased because of the absence of any parameters (effectively)
  • So, λ should be chosen carefully - not too big...
    • We look at some automatic ways to select λ later in the course

Regularized linear regression

  • Previously, we looked at two algorithms for linear regression
    • Gradient descent
    • Normal equation
  • Our linear regression with regularization is shown below



  • Previously, gradient descent would repeatedly update the parameters θj, where j = 0,1,2...n simultaneously
    • Shown below



  • We've got the θ0 update here shown explicitly
    • This is because for regularization we don't penalize θso treat it slightly differently
  • How do we regularize these two rules?
    • Take the term and add λ/m * θj
      • Sum for every θ (i.e. j = 0 to n)
    • This gives regularization for gradient descent
  • We can show using calculus that the equation given below is the partial derivative of the regularized J(θ)



  • The update for θ
    • θj gets updated to 
      • θ- α * [a big term which also depends on θj] 
  • So if you group the θterms together


  • The term 
  •     
    • Is going to be a number less than 1 usually
    • Usually learning rate is small and m is large
      • So this typically evaluates to (1 - a small number)
      • So the term is often around 0.99 to 0.95
  • This in effect means θgets multiplied by 0.99
    • Means the squared norm of θa little smaller
    • The second term is exactly the same as the original gradient descent 

Regularization with the normal equation

  • Normal equation is the other linear regression model
    • Minimize the J(θ) using the normal equation
    • To use regularization we add a term (+ λ [n+1 x n+1]) to the equation
      • [n+1 x n+1] is the n+1 identity matrix 



Regularization for logistic regression


  • We saw earlier that logistic regression can be prone to overfitting with lots of features 
  • Logistic regression cost function is as follows;


  • To modify it we have to add an extra term
  • This has the effect of penalizing the parameters θ1, θ2 up to θ
    • Means, like with linear regression, we can get what appears to be a better fitting lower order hypothesis 
  • How do we implement this?
    • Original logistic regression with gradient descent function was as follows
  • Again, to modify the algorithm we simply need to modify the update rule for θ1, onwards
    • Looks cosmetically the same as linear regression, except obviously the hypothesis is very different


Advanced optimization of regularized linear regression

  • As before, define a costFunction which takes a θ parameter and gives jVal and gradient back



  • use fminunc
    • Pass it an @costfunction argument
    • Minimizes in an optimized manner using the cost function
  • jVal
    • Need code to compute J(θ)
      • Need to include regularization term
  • Gradient
    • Needs to be the partial derivative of J(θ) with respect to θi
    • Adding the appropriate term here is also necessary



  • Ensure summation doesn't extend to to the lambda term! 
    • It doesn't, but, you know, don't be daft!










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