The AdaBoost algorithm =1/n for i =1,...,n 1) At the m th iteration we find (any) classifier h(x; ˆθ m ) for which the weighted classification error m

Transcription

1 ) Set W () i The AdaBoost algorithm =1/n for i =1,...,n 1) At the m th iteration we find (any) classifier h(x; ˆθ m ) for which the weighted classification error m m =.5 1 n W (m 1) i y i h(x i ; 2 ˆθ m ) is better than chance. i=1 2) The new component is assigned votes based on its error: ˆα m =.5 log( (1 m )/ m ) 3) The weights are updated according to (Z m is chosen so that the new weights sum to one): W (m) i = 1 Z m W (m) i W (m 1) i exp{ y iˆα m h(x i ; ˆθ m ) } Tommi Jaakkola, MIT CSAIL 18

2 Adaboost properties: exponential loss After each boosting iteration, assuming we can find a component classifier whose weighted error is better than chance, the combined classifier ĥ m (x) =ˆα 1 h(x; ˆθ 1 )+...+ˆα m h(x; ˆθ m ) is guaranteed to have a lower exponential loss over the training examples exponential loss number of iterations Tommi Jaakkola, MIT CSAIL 2

3 Adaboost properties: training error The boosting iterations also decrease the classification error of the combined classifier ĥ m (x) =ˆα 1 h(x; ˆθ 1 )+...+ˆα m h(x; ˆθ m ) over the training examples training error number of iterations Tommi Jaakkola, MIT CSAIL 21

4 Adaboost properties: training error cont d The training classification error has to go down exponentially fast if the weighted errors of the component classifiers, k, are strictly better than chance k <.5 m err(ĥm) 2 k (1 k ) k= training error number of iterations Tommi Jaakkola, MIT CSAIL 22

5 Adaboost properties: weighted error Weighted error of each new component classifier k =.5 1 n W (k 1) i y i h(x i ; 2 ˆθ k ) i=1 tends to increase as a function of boosting iterations weighted training error number of iterations Tommi Jaakkola, MIT CSAIL 23

6 How Will Test Error Behave? (A First Guess) 1.8 error test train # of rounds ( T) expect: training error to continue to drop (or reach zero) test error to increase when H final becomes too complex Occam s razor overfitting hard to know when to stop training

7 Technically... with high probability: generalization error training error + Õ ( dt m ) bound depends on m = #trainingexamples d = complexity of weak classifiers T = # rounds generalization error = E [test error] predicts overfitting

8 Typical performance Training and test errors of the combined classifier ĥ m (x) =ˆα 1 h(x; ˆθ 1 )+...+ˆα m h(x; ˆθ m ) training/test errors number of iterations Why should the test error go down after we already have zero training error? Tommi Jaakkola, MIT CSAIL 24

9 AdaBoost and margin We can write the combined classifier in a more useful form by dividing the predictions by the total number of votes : ĥ m (x) = ˆα 1h(x; ˆθ 1 )+...+ˆα m h(x; ˆθ m ) ˆα ˆα m This allows us to define a clear notion of voting margin that the combined classifier achieves for each training example: margin(x i )=y i ĥm(x i ) The margin lies in [ 1, 1] and is negative for all misclassified examples. Tommi Jaakkola, MIT CSAIL 25

10 AdaBoost and margin Successive boosting iterations still improve the majority vote or margin for the training examples margin(x i ) = y i ˆα 1 h(x i ; ˆθ 1 )+...+ˆα m h(x i ; ˆθ m ) ˆα ˆα m Cumulative distributions of margin values: iterations 1 iterations Tommi Jaakkola, MIT CSAIL 26

11 AdaBoost and margin Successive boosting iterations still improve the majority vote or margin for the training examples margin(x i ) = y i ˆα 1 h(x i ; ˆθ 1 )+...+ˆα m h(x i ; ˆθ m ) ˆα ˆα m Cumulative distributions of margin values: iterations 5 iterations Tommi Jaakkola, MIT CSAIL 27

12 Can we improve the combination? As a result of running the boosting algorithm for m iterations, we essentially generate a new feature representation for the data φ i (x) =h(x; ˆθ i ),i=1,...,m Perhaps we can do better by separately estimating a new set of votes for each component. In other words, we could estimate a linear classifier of the form f(x; α) =α 1 φ 1 (x)+...α m φ m (x) where each parameter α i can be now any real number (even negative). The parameters would be estimated jointly rather than one after the other as in boosting. Tommi Jaakkola, MIT CSAIL 28

13 Can we improve the combination? We could use SVMs in a postprocessing step to reoptimize f(x; α) =α 1 φ 1 (x)+...α m φ m (x) with respect to α 1,...,α m. This is not necessarily a good idea training/test errors.8.6 training/test errors.8.6 typically number of iterations boosting number of components svm postprocessing Tommi Jaakkola, MIT CSAIL 29

14 Practical Advantages of AdaBoost fast simple and easy to program no parameters to tune (except T ) flexible can combine with any learning algorithm no prior knowledge needed about weak learner provably effective, provided can consistently find rough rules of thumb shift in mind set goal now is merely to find classifiers barely better than random guessing versatile can use with data that is textual, numeric, discrete, etc. has been extended to learning problems well beyond binary classification

15 Caveats performance of AdaBoost depends on data and weak learner consistent with theory, AdaBoost can fail if weak classifiers too complex overfitting weak classifiers too weak (γ t too quickly) underfitting low margins overfitting empirically, AdaBoost seems especially susceptible to uniform noise

16 Multiclass Problems say y Y where Y = k direct approach (AdaBoost.M1): [with Freund] D t+1 (i) = D t(i) Z t h t : X Y H final (x) =argmax y Y { e α t if y i = h t (x i ) e α t if y i h t (x i ) t:h t (x)=y can prove same bound on error if t : ɛ t 1/2 in practice, not usually a problem for strong weak learners (e.g., C4.5) significant problem for weak weak learners (e.g., decision stumps) instead, reduce to binary α t

17 The One-Against-All Approach break k-class problem into k binary problems and solve each separately say possible labels are Y = {,,, } x 1 x 1 x 1 + x 1 x 1 x 2 x 2 x 2 x 2 + x 2 x 3 x 3 x 3 x 3 x 3 + x 4 x 4 x 4 + x 4 x 4 x 5 x 5 + x 5 x 5 x 5 to classify new example, choose label predicted to be most positive AdaBoost.MH problem: not robust to errors in predictions [with Singer]