# Section 6 : Cross Validation Yotam Shem-Tov Fall 2014 1/25 Yotam - PowerPoint PPT Presentation

## Section 6 : Cross Validation Yotam Shem-Tov Fall 2014 1/25 Yotam Shem-Tov STAT 239/ PS 236A In Sample prediction error There are two types of Prediction errors: In sample prediction error and out of sample prediction error. In sample

1. Section 6 : Cross − Validation Yotam Shem-Tov Fall 2014 1/25 Yotam Shem-Tov STAT 239/ PS 236A

2. In Sample prediction error There are two types of Prediction errors: In sample prediction error and out of sample prediction error. In sample prediction error: how well does the model explain the data which is used in order to estimate the model. Consider a sample, ( y , X ), and fit a model f ( · ) (for example a regression model), and denote the fitted values by ˆ y i . In order to determine how well the model fits the data, we need to choose some criterion, which is called the loss function, i.e L ( y i , ˆ y i ). standard loss functions: � MSE = 1 � N y i ) 2 , RMSE = 1 � N y i ) 2 i =1 ( y i − ˆ i =1 ( y i − ˆ N N 2/25 Yotam Shem-Tov STAT 239/ PS 236A

3. Out of sample prediction error How well can the model predict a value of y j given x j where observation j is not in the sample. This is referred to as the out of sample prediction error. How can we estimate the out of sample prediction error? The most commonly used method is Cross-Validation . 3/25 Yotam Shem-Tov STAT 239/ PS 236A

4. Cross-Validation Summary of the approach: 1 Split the data into a training set and a test set 2 Build a model on the training data 3 Evaluate on the test set 4 Repeat and average the estimated errors Cross-Validation is used for: 1 Choosing model parameters 2 Model selection 3 Picking which variables to include in the model 4/25 Yotam Shem-Tov STAT 239/ PS 236A

5. Cross-Validation There are 3 common CV methods, in all of them there is a trade-off between the bias and variance of the estimator. 1 Random sub-sampling CV 2 K-fold CV 3 Leave one out CV (LOOCV) My preferred method is Random sub-sampling CV . 5/25 Yotam Shem-Tov STAT 239/ PS 236A

6. Random sub-sampling CV 1 Randomly split the data into a test set and training set. 2 Fit the model using the training set, without using the test set at all! 3 Evaluate the model using the test set 4 Repeat the procedure multiple times and average the estimated errors (RMSE) What is the tuning parameter in this procedure? The fraction of the data which is used as a test set There is no common choice of fraction to use. My preferred choice is 50%, however this is arbitrary. 6/25 Yotam Shem-Tov STAT 239/ PS 236A

7. Random sub-sampling CV: Example Recall the dilemma of choosing a P-score model: with or without interactions. No interactions With interactions Treatment Treatment Control Control 4 4 3 3 Density Density 2 2 1 1 0 0 −0.2 0.2 0.4 0.6 0.8 0.0 0.5 1.0 P−score P−score 7/25 Yotam Shem-Tov STAT 239/ PS 236A

8. Random sub-sampling CV: Example We can use CV in order to choose between the two competing models. L0=100 # number of repetitions rmse.model.1 <- rmse.model.2 <- rep(NA,L0) a = data.frame(treat=treat,x) for (j in c(1:L0)){ id = sample(c(1:dim(d)[1]),round(dim(d)[1]*0.5)) ps.model1 <- glm(treat~(.),data=a[id,],family=binomial(link=logit)) ps.model2 <- glm(treat~(.)^2,data=a[id,],family=binomial(link=logit)) rmse.model.1[j]=rmse(predict(ps.model1,newdata=a[-id,], type="response"),a\$treat[-id]) rmse.model.2[j]=rmse(predict(ps.model2,newdata=a[-id,], type="response"), a\$treat[-id]) } 8/25 Yotam Shem-Tov STAT 239/ PS 236A

9. ● 0.50 ● ● 0.45 ● ● ● 0.40 0.35 0.30 0.25 0.20 Model 1 Model 2 9/25 Yotam Shem-Tov STAT 239/ PS 236A

10. Random sub-sampling CV: Example The results are in the table below: Model 1 Model 2 Mean 0.29 0.33 Median 0.29 0.32 It is clear that model 1, no interactions, has a lower out of sample prediction error. Model 2 (with interactions) over fits the data, and generates a model with a wrong P-score. The model includes too many covariates Note, it is also possible to examine other models that include some of the interactions, but not all of them 10/25 Yotam Shem-Tov STAT 239/ PS 236A

11. K Folds CV Randomly split the data into K folds (groups) Estimate the model using K − 1 folds Evaluate the model using the remaining fold. Repeat the process by the number of folds, K times Average the estimated errors across folds The choice of K , is a classic problem of bias-variance trade-off. What is the tuning parameter in this method? The number of folds , K . There is no common choice of K to use. Commonly used choices are, K = 10, and K = 20. The choice of K depends on the size of the sample, N . 11/25 Yotam Shem-Tov STAT 239/ PS 236A

12. The tuning parameter K folds, Choosing the number of folds, K ↑ K lower bias, higher variance ↓ K higher bias, lower variance Random sub-sampling, Choosing the fraction of the data in the test set ↓ fraction lower bias, higher variance ↑ fraction higher bias, lower variance 12/25 Yotam Shem-Tov STAT 239/ PS 236A

13. Leave one out CV (LOOCV) LOOCV is a specific case of K folds CV, where K = N Example in which there is an analytical formula for the LOOCV statistic The model: Y = X β + ε β = ( X ′ X ) − 1 X ′ y The OLS estimator: ˆ Define the hat matrix as, H = X ( X ′ X ) − 1 X ′ Denote the elements on the diagonal of H , as h i The LOOCV statistic is, n CV = 1 � ( e i / (1 − h i )) 2 n i =1 i ˆ β , and ˆ where e i = y i − x ′ β is the OLS estimator over the whole sample 13/25 Yotam Shem-Tov STAT 239/ PS 236A

14. CV in time series data The CV methods discussed so far do not work when dealing with time series data The dependence across observations generates a structure in the data, which will be violated by a random split of the data Solutions: An iterated approach of CV 1 Bootstrap 0.632 (?) 2 14/25 Yotam Shem-Tov STAT 239/ PS 236A

15. CV in time series data Summary of the iterated approach: 1 Build a model using the first M periods 2 Evaluate the model on period t = ( M + 1) : T 3 Build a model using the first M + 1 periods 4 Evaluate the model on period t = ( M + 2) : T 5 Continue iterating forward until, M + 1 = T 6 Average over the estimated errors 15/25 Yotam Shem-Tov STAT 239/ PS 236A

16. Example We want to predict the GDP growth rate in California in 2014 The available data is only the growth rates in in the years 1964 − 2013 consider the following three possible Auto-regression models: y t = α + β 1 y t − 1 1 y t = α + β 1 y t − 1 + β 2 y t − 2 2 y t = α + β 1 y t − 1 + β 2 y t − 2 + β 3 y t − 3 3 16/25 Yotam Shem-Tov STAT 239/ PS 236A

17. Example: The data ● 15 ● ● ● ●● ●● ●●● ● 10 ● ●● ● ● ● ● ● ● growth ● ● ● ● ●● ● ● ● ● ● ● ●● ● 5 ● ● ● ● ● ● ● ● ●●● ● ● 0 ● 1970 1980 1990 2000 2010 year 17/25 Yotam Shem-Tov STAT 239/ PS 236A

18. Example: estimation of the three models Model 1 Model 2 Model 3 Intercept 1 . 954 ∗ 1 . 935 ∗ 1 . 411 (0 . 841) (0 . 919) (0 . 977) Lag 1 0 . 717 ∗∗∗ 0 . 710 ∗∗∗ 0 . 716 ∗∗∗ (0 . 103) (0 . 149) (0 . 149) Lag 2 0 . 014 − 0 . 145 (0 . 150) (0 . 182) Lag 3 0 . 217 (0 . 150) R 2 0.505 0.509 0.534 Adj. R 2 0.495 0.487 0.502 Num. obs. 49 48 47 ∗∗∗ p < 0 . 001, ∗∗ p < 0 . 01, ∗ p < 0 . 05 18/25 Yotam Shem-Tov STAT 239/ PS 236A

19. Example: choice of model Which of the models will you choose? Will you use an F-test? What is your guess: which of the models will have a lower out of sample error , using CV? 19/25 Yotam Shem-Tov STAT 239/ PS 236A

20. Example: F-test I Note, in order to conduct an F-test, we need to drop the first 3 observations. This is in order to have the same data used in the estimation of all three models. Dropping the first 3 observations, might biased our results in favour of models 2 and 3, relative to model 1. Analysis of Variance Table Model 1: y ~ lag1 + lag2 Model 2: y ~ lag1 + lag2 + lag3 Res.Df RSS Df Sum of Sq F Pr(>F) 1 44 330.02 2 43 314.58 1 15.438 2.1102 0.1536 20/25 Yotam Shem-Tov STAT 239/ PS 236A

21. Example: F-test II Analysis of Variance Table Model 1: y ~ lag1 Model 2: y ~ lag1 + lag2 Res.Df RSS Df Sum of Sq F Pr(>F) 1 45 330.03 2 44 330.02 1 0.012439 0.0017 0.9677 21/25 Yotam Shem-Tov STAT 239/ PS 236A

22. Example: F-test III Analysis of Variance Table Model 1: y ~ lag1 Model 2: y ~ lag1 + lag2 + lag3 Res.Df RSS Df Sum of Sq F Pr(>F) 1 45 330.03 2 43 314.58 2 15.45 1.0559 0.3567 22/25 Yotam Shem-Tov STAT 239/ PS 236A

23. Example: CV Results We used the iterative approach, as this is time series data M is the number of periods used for fitting the model before starting the CV procedure. The average RMSE are, Model 1 Model 2 Model 3 M = 5 27.266 27.078 26.994 M = 10 29.770 29.586 29.474 M = 15 33.106 32.924 32.797 Among Model 1 and Model 2 only, which is preferable? 23/25 Yotam Shem-Tov STAT 239/ PS 236A

24. The tuning parameter in time series CV What is the bias-variance trade-off in the choice of M ? Choice of M ↑ M lower bias, higher variance ↓ M higher bias, lower variance 24/25 Yotam Shem-Tov STAT 239/ PS 236A

25. Additional readings For a survey of cross-validation results, see Arlot and Celisse (2010), http://projecteuclid.org/euclid.ssu/1268143839 25/25 Yotam Shem-Tov STAT 239/ PS 236A
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