1. Data Mining - 2011 - Volinsky - Columbia University 1 Chapter 4.2 Regression Topics Credits Hastie, Tibshirani, Friedman Chapter 3 Padhraic Smyth Lecture Notes Wolfgang Jank Lecture Notes

2. Data Mining - 2011 - Volinsky - Columbia University 2 Regression Review Linear Regression models a numeric outcome as a linear function of several predictors. It is the king of all statistical and data mining models –ease of interpretation –mathematically concise –tends to perform well for prediction, even under violations of assumptions Characteristics –numeric response - ideally real valued –numeric predictors- but not necessarily

3. Data Mining - 2011 - Volinsky - Columbia University 3 Linar Regression Model Basic model: you are not modelling y, but you are modelling the mean of y for a given x! Simple Regression - one x. –easy to describe, good for mathematics, but not used often in data mining Multiple regression - many x - – response surface is a plane…harder to conceptualize Useful as a baseline model

4. Data Mining - 2011 - Volinsky - Columbia University 4 Linear Regression Model Assumptions: –linearity –constant variance –normality of errors residuals ~ Normal(mu,sigma^2) Assumptions must be checked, –but if inference is not the goal, you can accept some deviation from assumptions (don’t’ tell the statisticians I said that!) Multicollinearity also an issue –creates unstable estimates

5. Data Mining - 2011 - Volinsky - Columbia University 5 Fitting the Model We can look at regression as a matrix problem We want a score function which minimizes “a”: = which is minimized by

6. Fitting models: in-sample Minimize the sum of the squared errors: S =  e 2 = e’ e = (y – X a)’ (y – X a) = y’ y – a’ X’ y – y’ X a + a’ X’ X a = y’ y – 2 a’ X’ y + a’ X’ X a Take derivative of S with respect to a: dS/da = -2X’y + 2 X’ X a Set this to 0 to find the (minimum) of S as a function of a…  - 2X’y + 2 X’ X a = 0  X’Xa = X’ y  a = ( X’ X ) -1 X’ y  Prediction follows easily: Data Mining - 2011 - Volinsky - Columbia University 6

7. Fitting regression: out-of-sample Can also optimize “a” based on a hold-out sample and a search over all “a”s –But how to search over all values of all a’s? –This will minimize MSE – might give a different answer MSE=Bias + Variance Because of the nice algebraic form, typically in- sample is used –But different loss function may change things –R 2 measures a ratio between regression sum of squares - how much of the variance does the regression explain, and the total sum of squares - how much variation is there altogether –If it is close to 1, your fit is good. But be careful. Data Mining - 2011 - Volinsky - Columbia University 7

8. 8 Limitations of Linear Regression True relationship of X and Y might be non-linear –Suggests generalizations to non-linear models Correlation/Collinearity among the X variables –Can cause numerical instability –Problems in interpretability (identifiability) Includes all variables in the model… –But what if p=100 and only 3 variables are related to Y?

9. Data Mining - 2011 - Volinsky - Columbia University 9 Checking assumptions linearity –look to see if transformations make relationships ‘more’ linear normality of errors –Histograms and qqplots Non-constant variance –Beware of ‘fanning’ residuals Time effects –Can be revealed in an ordering plot Influence –Use hat matrix

10. Data Mining - 2011 - Volinsky - Columbia University 10 Checking influence Influence H is called the hat matrix (why?): The element of H for a given observation is its influence The leverage h i quantifies the influence that the observed response y i has on its predicted value y It measures the distance between the X values for the i th case and the means of the X values for all n cases. influence h i is a number between 0 and 1 inclusive. ^

11. Influence Measures for Linear Model There are a few quite influential (and extreme) points… What to do? 11 Data Mining - 2011 - Volinsky - Columbia University

12. 12 Diagnostic Plots

13. Data Mining - 2011 - Volinsky - Columbia University 13

14. Data Mining - 2011 - Volinsky - Columbia University 14 Model selection: finding the best k variables If noisy variables are included in the model, it can effect the overall performance. Best to remove an predictors which have no effect, lest random patterns look significant. Searching all possible models –How many are there? –Heuristic search is used to search over model space: Forward or backward stepwise search Leaps and bound techniques do exhaustive search –In-sample: penalize for complexity (AIC, BIC, Mallow’s C p ) –Out-of-sample: use cross validation

15. Data Mining - 2011 - Volinsky - Columbia University 15 R ‘step’: uses AIC

16. Leaps output Data Mining - 2011 - Volinsky - Columbia University 16 R ‘leaps’ : uses C p

17. Data Mining - 2011 - Volinsky - Columbia University 17 Generalizing Linear Regression

18. Data Mining - 2011 - Volinsky - Columbia University 18 Complexity versus Goodness of Fit x y Training data

19. Data Mining - 2011 - Volinsky - Columbia University 19 Complexity versus Goodness of Fit x y x y Too simple? Training data

20. Data Mining - 2011 - Volinsky - Columbia University 20 Complexity versus Goodness of Fit x y x y x y Too simple? Too complex ? Training data

21. Data Mining - 2011 - Volinsky - Columbia University 21 Complexity versus Goodness of Fit x y x y x y x y Too simple? Too complex ?About right ? Training data

22. Data Mining - 2011 - Volinsky - Columbia University 22 Complexity and Generalization S train (  ) S test (  ) Complexity = degrees of freedom in the model (e.g., number of variables) Score Function e.g., squared error Optimal model complexity

23. Data Mining - 2011 - Volinsky - Columbia University 23 Non-linear models, linear in parameters We can add additional polynomial terms in our equations, non-linear functional form, but linear in the parameters (so still referred to as “linear regression”) –We can just treat the x i x j terms as additional fixed inputs –In fact we can add in any non-linear input functions!, e.g. Comments: -Number of parameters can explode => greater chance of overfitting –Adding complexity: must use penalties!

24. Data Mining - 2011 - Volinsky - Columbia University Non-linear (both model and parameters) We can generalize further to models that are nonlinear in all aspects where the g’s are non-linear functions (k of them) This is called a Neural Network (we’ll talk about it later) Closed form (analytical) solutions are rare. This is a a multivariate non-linear optimization problem (which may be quite difficult!) 24

25. Data Mining - 2011 - Volinsky - Columbia University 25 Generalizing Regression Generalized Linear Models (GLM) independent RV with distribution based on the error term linear combination of the predictors function which connects the two GLMs are defined by error structure (Gaussian, Poisson, Binomial) linear predictor (single variables, interactions, polynomials) link function (identity, log, reciprocal)

26. Data Mining - 2011 - Volinsky - Columbia University 26 Logistic Regression Logistic regression is the most common GLM. response in this case is binary (0,1). (Y follows a bernoulli or Binomial distribution) we model the probability of a 1 (p) occurring. for mathematical convenience, we model the odds: –p/(1-p) –log odds are even better - logit function –scales on the real line, rather than [0,1] Deviance: -2 x (difference in log-likelihood from saturated model)

27. Logistic Regression Interpretation of coefficients changes! Data Mining - 2011 - Volinsky - Columbia University 27

28. Data Mining - 2011 - Volinsky - Columbia University 28 Logistic example womensrole data (R handbook) –Survey in 1975: “Women should take care of running their homes and leave running the coutnry up to men” education sex agree disagree 1 0 Male 4 2 2 1 Male 2 0 3 2 Male 4 0 4 3 Male 6 3 5 4 Male 5 5 6 5 Male 13 7 7 6 Male 25 9 8 7 Male 27 15 9 8 Male 75 49 10 9 Male 29 29 11 10 Male 32 45 …

29. Data Mining - 2011 - Volinsky - Columbia University 29 Womensrole Logistic fit

30. Data Mining - 2011 - Volinsky - Columbia University 30 Other GLMs Another useful GLM is for count data –model Y ~ Poisson(lambda) –link is log(Y) –Also called ‘log-linear’ models –Typically used for counts: People at a store Calls at a help center Spams in an hour

31. Data Mining - 2011 - Volinsky - Columbia University 31 Shrinkage Models: Ridge Regression Variable selection is a binary process –That makes it high variance: small changes can effect final model –Can we have a more continuous process, where each variable is ‘partly’ included? Ridge regression “shrinks” coefficients on by imposing a penalty for the model “size” Minimize the penalized sum of squares:  is a complexity parameter which controls the amount of shrinkage - the larger  is, the more the coefficients are shrunk towards 0.

32. Data Mining - 2011 - Volinsky - Columbia University 32 Ridge Regression Model is imposing a penalty on the coefficient size Since a’s depend on the units, care must be taken to standardize inputs. Also, you can show that the ridge estimates are a linear function of y: this adds a positive constant to the diagonal and allows inverision even if the matrix is not full rank –So, can be used in cases where p > n! In general: increasing bias, decreasing variance –Often decreases MSE

33. Data Mining - 2011 - Volinsky - Columbia University 33 Ridge coefficients df( ) is a one-to-one monotone function of  such that df( ) ranges from 0 to p. = 0; s=p : least squares solution; p degrees of freedom = inf; s=0; heaviest shrinkage; all parameter estimates = 0; zero degrees of freedom Look at plot as a function of degrees of freedom df( )

34. Data Mining - 2011 - Volinsky - Columbia University 34 Lasso Very similar to ridge with one important difference: L 2 penalty replaced by L 1 has an interesting effect on the profile plot: –if lambda is large then estimates go to zero –continuous variable selection –s=1 is least squares answer –s=0 all estimates are 0 –s=0.5 was the value chosen by cross validation

35. lasso coefficients Note how parameters shrink to zero! This is the appeal of lasso (in addition to good performance) Data Mining - 2011 - Volinsky - Columbia University 35 s = df( ) / p

36. Principal Components Regression Create PC from the original data vectors and use them in any of the above regression schemes Removes the ‘less important’ parts of the data space, while creating a reduced data set Since each PC is a linear combination of the original variables, we can express the solution in terms of the initial coefficients. Data Mining - 2011 - Volinsky - Columbia University 36

37. Comparison of results (prostate data) TermLSBest Subset RidgeLassoPCR Intercept 2.4652.4772.4522.4682.497 Lcavol 0.6800.7400.4200.5330.543 Lwight 0.2360.3160.2380.1690.289 Age -0.141-0.046-0.152 Lbph 0.2100.1620.0020.214 Svi 0.3050.2270.0940.315 Lcp -0.2880.000-0.051 Gleason -0.0210.0400.232 Pgg45 0.2670.133-0.056 Test Error 0.5210.492 0.4790.449 Std Error 0.1790.1430.1650.1640.105 Data Mining - 2011 - Volinsky - Columbia University 37 Cross validation allows all of these different methods to be comparable to each other

38. Nonparametric Modeling A nonparametric model does not assume any parameters to be estimated (thus the name nonparametric) –Its general form is Y = f(X) + ε –Typically, we only assume that f() is some smooth, continuous function –Also, we typically assume independent and identically distributed errors, ε~N(0,σ^2), but that’s not necessary. –1-D nonparametric regression = density estimation 38 Data Mining - 2011 - Volinsky - Columbia University

39. Advantages & Disadvantages Advantage –More flexibility leads to better data-fit, often also to better predictive capabilities –Smoothness can also lead to entirely new concepts, such as dynamics (via derivatives) and thus to flexible differential equation models, etc Disadvantage –Much more complexity, hard to explain 39 Data Mining - 2011 - Volinsky - Columbia University

40. Fitting Nonparametric models How do we estimate the function f()? –Restrictions on f: smoothness, continuity, existence of the first and second derivatives –options for estimating f include scatterplot smoothers, regression splines, smoothing splines, B-splines, thin- plate splines, wavelets, and many, many more… –one particularly popular option, the smoothing spline 40 Data Mining - 2011 - Volinsky - Columbia University

41. Splines Splines are piecewise polynomials smoothly connected together. The joining points of the polynomial pieces are called knots. Smoothing splines are splines that are penalized against too much local variability (and thus appear smoother) –Must be differentiable at the knots –linear spline: 0-times differentiable –cubic spline: twice differentiable 41 Data Mining - 2011 - Volinsky - Columbia University

42. Piecewise Polynomial cont. Piecewise constant and piecewise linear “Knots” 42 Data Mining - 2011 - Volinsky - Columbia University

43. Spline cont. (Linear Spline) 43 Data Mining - 2011 - Volinsky - Columbia University

44. Spline cont. (Cubic Spline) Cubic spline 44 Data Mining - 2011 - Volinsky - Columbia University

45. Definition of Smoothing Splines Smoothing Splines arise as the solution to the following simple regression problem –Find a piecewise polynomial f(x) with smooth breakpoints –f(x) minimizes the penalized sum-of-squares fitcurvature 45 Data Mining - 2011 - Volinsky - Columbia University

46. Example of Smoothing Splines Two Smoothing Splines fit to the Prestige Data –Little smoothing, λ small (red line) –Heavy smoothing, λ large (blue line) 46 Data Mining - 2011 - Volinsky - Columbia University

47. The smoothing parameter The magnitude of λ affects the quality of the smoother; many ad-hoc approaches to find a “good” smoothing parameter –Visual trial and error –Minimize mean-squared error of the fit –Cross-validation, optimization on hold-out sample, etc 47 Data Mining - 2011 - Volinsky - Columbia University

48. Prestige Data Revisited Education (X1) and Income (X2) influence the perceived Prestige (Y) of a profession Is there a linear relationship between the X’s and Y? If we’re not sure of the type of relationship between X and Y, nonparametric regression can be a very useful exploratory tool. 48 Data Mining - 2011 - Volinsky - Columbia University

49. Additive Model Estimates Parametric coefficients: Estimate std. err. t ratio Pr(>|t|) constant 46.833 0.6889 67.98 <2e-16 Approximate significance of smooth terms: edf chi.sq p-value s(income) 3.118 58.12 8.39e-10 s(education) 3.177 152.79 <2e-16 R-sq.(adj) = 0.836 Deviance explained = 84.7% GCV score = 52.143 Intercept! Inference for Income and Education, similar to F-test Measures of model fit 49 Data Mining - 2011 - Volinsky - Columbia University

50. Compare to Classical Regression Parametric coefficients: Estimate std. err. t ratio Pr(>|t|) (Intercept) -6.8478 3.219 -2.127 0.0359 income 0.0013612 0.000224 6.071 2.36e-08 education 4.1374 0.3489 11.86 <2e-16 R-sq.(adj) = 0.794 Deviance explained = 79.8% GCV score = 62.847 Better model fit for the nonparametric model!! 50 Data Mining - 2011 - Volinsky - Columbia University

51. Function Estimates from Additive Regression Model What is the nature of the relationship of the individual predictor variables and prestige? 51 Data Mining - 2011 - Volinsky - Columbia University