# Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler

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1 Machine Learning and Data Mining Ensembles of Learners Prof. Alexander Ihler

2 Ensemble methods Why learn one classifier when you can learn many? Ensemble: combine many predictors (Weighted) combina<ons of predictors May be same type of learner or different Various options for getting help: Who wants to be a millionaire?

3 Simple ensembles CommiCees Unweighted average / majority vote Weighted averages Upweight becer predictors Ex: Classes: 1, 1, weights alpha: ŷ 1 = f 1 (x 1,x 2, ) ŷ 2 = f 2 (x 1,x 2, ) => ŷ e = sign( i ŷ i )

4 Stacked ensembles Train a predictor of predictors Treat individual predictors as features ŷ 1 = f 1 (x 1,x 2, ) ŷ 2 = f 2 (x 1,x 2, ) => ŷ e = f e (ŷ 1, ŷ 2, ) Similar to mul<layer perceptron idea Special case: binary, f e linear => weighted vote Can train stacked learner f e on valida<on data Avoids giving high weight to overfit models

5 Mixtures of experts Can make weights depend on x Weight z (x) indicates exper<se Combine using weighted average (or even just pick largest) Example 4.5 Weighted average: Weights: (multi) logistic regression If loss, learners, weights are all differentiable, can train jointly Mixture of three linear predictor experts

6 Machine Learning and Data Mining Ensembles: Bagging Prof. Alexander Ihler

7 Ensemble methods Why learn one classifier when you can learn many? CommiCee : learn K classifiers, average their predic<ons Bagging = bootstrap aggrega<on Learn many classifiers, each with only part of the data Combine through model averaging Remember overfi[ng: memorize the data Used test data to see if we had gone too far Crossvalida<on Make many splits of the data for train & test Each of these defines a classifier Typically, we use these to check for overfi[ng Could we instead combine them to produce a becer classifier?

8 Bagging Bootstrap Create a random subset of data by sampling Draw m of the m samples, with replacement (some variants w/o) Some data le_ out; some data repeated several <mes Bagging Repeat K <mes Create a training set of m m examples Train a classifier on the random training set To test, run each trained classifier Each classifier votes on the output, take majority For regression: each regressor predicts, take average Notes: Some complexity control: harder for each to memorize data Doesn t work for linear models (average of linear func<ons is linear func<on ) Perceptrons OK (linear threshold = nonlinear)

9 Bias / variance The world Data we observe We only see a licle bit of data Can decompose error into two parts Bias error due to model choice Can our model represent the true best predictor? Gets becer with more complexity Variance randomness due to data size BeCer w/ more data, worse w/ complexity Predictive Error (High bias) (High variance) Error on test data Model Complexity

10 Bagged decision trees Randomly resample data Learn a decision tree for each No max depth = very flexible class of func<ons Learner is low bias, but high variance Full data set Sampling: simulates equally likely data sets we could have observed instead, & their classifiers

11 Bagged decision trees Average over collec<on Classifica<on: majority vote Reduces memoriza<on effect Not every predictor sees each data point Lowers effec<ve complexity of the overall average Usually, becer generaliza<on performance Intui<on: reduces variance while keeping bias low Full data set Avg of 5 trees Avg of 25 trees Avg of 100 trees

12 Bagging in Python # Load data set X, Y for training the ensemble m,n = X.shape classifiers = [ None ] * nbag # Allocate space for learners for i in range(nbag): ind = np.floor( m * np.random.rand(nuse) ).astype(int) # Bootstrap sample a data set: Xi, Yi = X[ind,:], Y[ind] # select the data at those indices classifiers[i] = ml.myclassifier(xi, Yi) # Train a model on data Xi, Yi # test on data Xtest mtest = Xtest.shape[0] predict = np.zeros( (mtest, nbag) ) # Allocate space for predictions from each model for i in range(nbag): predict[:,i] = classifiers[i].predict(xtest) # Apply each classifier # Make overall prediction by majority vote predict = np.mean(predict, axis=1) > 0 # if 1 vs 1

13 Random forests Bagging applied to decision trees Problem With lots of data, we usually learn the same classifier Averaging over these doesn t help! Introduce extra varia<on in learner At each step of training, only allow a subset of features Enforces diversity ( best feature not available) Keeps bias low (every feature available eventually) Average over these learners (majority vote) # in FindBestSplit(X,Y): for each of a subset of features for each possible split Score the split (e.g. information gain) Pick the feature & split with the best score Recurse on left & right splits

14 Summary Ensembles: collec<ons of predictors Combine predic<ons to improve performance Bagging Bootstrap aggrega<on Reduces complexity of a model class prone to overfit In prac<ce Resample the data many <mes For each, generate a predictor on that resampling Plays on bias / variance trade off Price: more computa<on per predic<on

15 Machine Learning and Data Mining Ensembles: Gradient Boosting Prof. Alexander Ihler

16 Ensembles Weighted combina<ons of predictors CommiCee decisions Trivial example Equal weights (majority vote / unweighted average) Might want to weight unevenly upweight becer predictors Boos<ng Focus new learners on examples that others get wrong Train learners sequen<ally Errors of early predic<ons indicate the hard examples Focus later predic<ons on ge[ng these examples right Combine the whole set in the end Convert many weak learners into a complex predictor

17 Gradient boos<ng Learn a regression predictor Compute the error residual Learn to predict the residual Learn a simple predictor Then try to correct its errors

18 Gradient boos<ng Learn a regression predictor Compute the error residual Learn to predict the residual Combining gives a better predictor Can try to correct its errors also, & repeat

19 Gradient boos<ng Learn sequence of predictors Sum of predictions is increasingly accurate Predictive function is increasingly complex Data & prediction function Error residual

20 Gradient boos<ng Make a set of predic<ons ŷ[i] The error in our predic<ons is J(y,ŷ) For MSE: J(.) = ( y[i] ŷ[i] ) 2 We can adjust ŷ to try to reduce the error ŷ[i] = ŷ[i] alpha f[i] f[i] ¼ rj(y, ŷ) = (y[i]ŷ[i]) for MSE Each learner is es<ma<ng the gradient of the loss f n Gradient descent: take sequence of steps to reduce J Sum of predictors, weighted by step size alpha

21 Gradient boos<ng in Python # Load data set X, Y learner = [None] * nboost # storage for ensemble of models alpha = [1.0] * nboost # and weights of each learner mu = Y.mean() # often start with constant mean predictor dy = Y mu # subtract this prediction away for k in range( nboost ): learner[k] = ml.myregressor( X, dy ) # regress to predict residual dy using X alpha[k] = 1.0 # alpha: learning rate or step size # smaller alphas need to use more classifiers, but may predict better given enough of them # compute the residual given our new prediction: dy = dy alpha[k] * learner[k].predict(x) # test on data Xtest mtest = Xtest.shape[0] predict = np.zeros( (mtest,) ) mu # Allocate space for predictions & add 1st (mean) for k in range(nboost): predict = alpha[k] * learner[k].predict(xtest) # Apply predictor of next residual & accum

22 Summary Ensemble methods Combine multiple classifiers to make better one Committees, average predictions Can use weighted combinations Can use same or different classifiers Gradient Boosting Use a simple regression model to start Subsequent models predict the error residual of the previous predictions Overall prediction given by a weighted sum of the collection

23 Machine Learning and Data Mining Ensembles: Boosting Prof. Alexander Ihler

24 Ensembles Weighted combinations of classifiers Committee decisions Trivial example Equal weights (majority vote) Might want to weight unevenly upweight good experts Boosting Focus new experts on examples that others get wrong Train experts sequentially Errors of early experts indicate the hard examples Focus later classifiers on getting these examples right Combine the whole set in the end Convert many weak learners into a complex classifier

25 Boos<ng example Classes 1, 1 Original data set, D 1 Trained classifier Update weights, D 2 Trained classifier Update weights, D 3 Trained classifier

26 Aside: minimizing weighted error So far we ve mostly minimized unweighted error Minimizing weighted error is no harder: Unweighted average loss: For any loss (logistic MSE, hinge, ) Weighted average loss: For e.g. decision trees, compute weighted impurity scores: p(1) = total weight of data with class 1 p(1) = total weight of data with class 1 => H(p) = impurity

27 Boos<ng example Weight each classifier and combine them:.33 *.57 *.42 * > < 0 Combined classifier ) 1node decision trees decision stumps very simple classifiers

28 AdaBoost = adap<ve boos<ng Pseudocode for AdaBoost Classes {1, 1} # Load data set X, Y ; Y assumed 1 / 1 for i in range(nboost): learner[i] = ml.myclassifier( X, Y, weights=wts ) # train a weighted classifier Yhat = learner[i].predict(x) e = wts.dot( Y!= Yhat ) # compute weighted error rate alpha[i] = 0.5 * np.log( (1e)/e ) wts *= np.exp( alpha[i] * Y * Yhat ) # update weights wts /= wts.sum() # and normalize them # Final classifier: predict = np.zeros( (mtest,) ) for i in range(nboost): predict = alpha[i] * learner[i].predict(xtest) # compute contribution of each model predict = np.sign(predict) # and convert to 1 / 1 decision Notes e >.5 means classifier is not better than random guessing Y * Yhat > 0 if Y == Yhat, and weights decrease Otherwise, they increase

29 AdaBoost theory Minimizing classifica<on error was difficult For logis<c regression, we minimized MSE or NLL instead Idea: low MSE => low classifica<on error Example of a surrogate loss func<on AdaBoost also corresponds to a surrogate loss func<on Predic<on is yhat = sign( f(x) ) If same as y, loss < 1; if different, loss > 1; at boundary, loss=1 This loss func<on is smooth & convex (easier to op<mize) f(x)!= y f(x) = y

30 AdaBoost example: ViolaJones ViolaJones face detection algorithm Combine lots of very weak classifiers Decision stumps = threshold on a single feature Define lots and lots of features Use AdaBoost to find good features And weights for combining as well

31 Haar wavelet features Four basic types. They are easy to calculate. The white areas are subtracted from the black ones. A special representation of the sample called the integral image makes feature extraction faster.

32 Training a face detector Wavelets give ~100k features Each feature is one possible classifier To train: iterate from 1:T Train a classifier on each feature using weights Choose the best one, find errors and reweight This can take a long time (lots of classifiers) One way to speed up is to not train very well Rely on adaboost to fix even weaker classifier Lots of other tricks in real ViolaJones Cascade of decisions instead of weighted combo Apply at multiple image scales Work to make computationally efficient

33 Summary Ensemble methods Combine multiple classifiers to make better one Committees, majority vote Weighted combinations Can use same or different classifiers Boosting Train sequentially; later predictors focus on mistakes by earlier Boosting for classification (e.g., AdaBoost) Use results of earlier classifiers to know what to work on Weight hard examples so we focus on them more Example: ViolaJones for face detection

(Sub)Gradient Descent CMSC 422 MARINE CARPUAT marine@cs.umd.edu Figures credit: Piyush Rai Logistics Midterm is on Thursday 3/24 during class time closed book/internet/etc, one page of notes. will include

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