Transcript Topic 10 - Ensemble Methods Data Mining - Volinsky - 2011 - Columbia University.

Topic 10 - Ensemble Methods
Data Mining - Volinsky - 2011 - Columbia University
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Ensemble Models - Motivation
• Remember this picture?
• Always looking for balance between low complexity (‘good on
average’ but bad for prediction) and high complexity (‘good for
specific cases’ but might overfit)
• By combining many different models, ensembles make it easier to
hit the ‘sweet spot’ of modelling.
• Best for models to draw from diverse, independent opinions
– Wisdom Of Crowds
Stest(q)
Strain(q)
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Ensemble Methods - Motivation
• Models are just models.
– Usually not true!
– The truth is often much more complex than any single
model can capture.
– Combinations of simple models can be arbitrarily
complex. (e.g. spam/robots models, neural nets, splines)
• Notion: An average of several measurements is often more
accurate and stable than a single measurement
Accuracy: how well the model does for estimation and
prediction
Stability: small changes in inputs have little effect on outputs
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Ensemble Methods – How They Work
• The ensemble predicts a target value as an average or a vote
of the predictions (of several individual models)...
– Each model is fit independently of the others
– Final prediction is a combination of the independent predictions of
all models
• For an continuous target, an ensemble averages predictions
– Usually weighted
• For a categorical target (classification), an ensemble may
average the probabilities of the target values…or may use
‘voting’.
– Voting classifies a case into the class that was selected most by
individual models
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Ensemble Models – Why they work
• Voting example
–
–
–
–
5 independent classifiers
70% accuracy for each
Use voting…
What is the probability that the ensemble model
is correct?
• Lets simulate it
– What about 100 examples?
– (not a realistic example, why?)
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Ensemble Schemes
• The beauty is that you can average together models of any kind!!!
• Don’t need fancy schemes – just average!
• But there are fancy schemes: each one has various ways of fitting
many models to the same data, and use voting or averaging
– Stacking (Wolpert 92): fit many leave-1-out models
– Bagging (Breiman 96) build models on many permutations of original
data
– Boosting (Freund & Shapire 96): iteratively re-model, using re-weighted
data based on errors from previous models…
– Arcing (Breiman 98), Bumping (Tibshirani 97), Crumpling (Anderson &
Elder 98) , Born-Again (Breiman 98):
– Bayesian Model Averaging - near to my heart…
• We’ll explore BMA, bagging and boosting…
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Ensemble Methods – Bayesian Model
Averaging
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Model Averaging
• Idea: account for inherent variance of the model selection
process
• Posterior Variance =
Within-Model Variance + Between-Model Variance
• Data-driven model selection is risky: “Part of the evidence is
spent specify the model” (Leamer, 1978)
• Model-based inferences can be over-precise
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Model Averaging
• For some quantity of interest D: avg over all Models M, given
the data D:
Pr( D| D)   Pr( D| M , D) Pr( M | D)
M
To calculate the first term properly, you need to integrate out model
parameters q,
Pr(D | M,D) 
Where ^q is the MLE.
 Pr(D | M,q,D)Pr(q | M,D)
ˆ, D)
 Pr(D | M,q

For the second term,
 note that
Pr( M k | D)  Pr(D | M k )Pr( M k )
BIC k  logPr(D | Mk )  log Pr(D | q k , M k ) 
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log(n)
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Bayesian Model Averaging
• The approximations on the previous page allow you to
calculate many posterior model probabilities quickly, and
gives you the weights to use for averaging.
• But, how do you know which models to average over?
– Example, regression with p parameters
– Each subset of p is a ‘model’
– 2p possible models!
• Idea:
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Model Averaging
• But how to find the best models without fitting all
models?
• Solution: Leaps and Bounds algorithm can find the best
model without fitting all models
– Goal: find the single best model for each model size
Don’t need to traverse
this part of the tree since
there is no way it can
beat AB
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BMA - Example
PMP = Posterior
Model Probability
Best Models
Score on holdout
data: BMA wins
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Ensemble Methods - Boosting
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Boosting…
• Different approach to model ensembles – mostly
for classification
• Observed: when model predictions are not highly
correlated, combining does well
• Big idea: can we fit models specifically to the
“difficult” parts of the data?
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Boosting— Algorithm
From HTF p. 339
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Example
• Courtesy M. Littman
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• Courtesy M. Littman
Example
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• Courtesy M. Littman
Example
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Boosting - Advantages
• Fast algorithms - AdaBoost
• Flexible – can work with any classification
algorithm
• Individual models don’t have to be good
– In fact, the method works best with bad models!
– (bad = slightly better than random guessing)
– Most common model – “boosted stumps”
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Boosting Example from HTF p. 302
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Ensemble Methods – Bagging / Stacking
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Bagging for Combining Classifiers
Bagging = Boostrap aggregating
• Big Idea:
– To avoid overfitting of specific dataset, fit model to “bootstrapped”
random sets of the data
• Bootstrap
– Random sample, with replacement, from the data set
– Size of sample = size of data
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–
–
–
X= (1,2,3,4,5,6,7,8,9,10)
B1=(1,2,3,3,4,5,6,6,7,8)
B2=(1,1,1,1,2,2,2,5,6,8)
…
• Bootstrap sample have the same statistical properties as
original data
• By creating similar datasets you can see how much stability
there is in your data. If there is a lack of stability, averaging
helps.
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Bagging
• Training data sets of size N
• Generate B “bootstrap” sampled data sets of size N
• Build B models (e.g., trees), one for each bootstrap sample
– Intuition is that the bootstrapping “perturbs” the data enough to
make the models more resistant to true variability
– Note: only ~62% of data included in any bootstrap sample
• Can use the rest as an out-of-sample estimate!
• For prediction, combine the predictions from the B models
– Voting or averaging based on“out-of-bag” sample
– Plus: generally improves accuracy on models such as trees
– Negative: lose interpretability
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HTF Bagging Example p 285
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Ensemble Methods – Random Forests
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Random Forests
• Trees are great, but
– As we’ve seen, they are “unstable”
– Also, trees are sensitive to the primary split, which can
lead the tree in inappropriate directions
– one way to see this: fit a tree on a random sample, or a
bootstrapped sample of the data -
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Example of Tree Instability
from G. Ridgeway, 2003
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Random Forests
• Solution:
– random forests: an ensemble of decision trees
– Similar to bagging: inject randomness to overcome instability
– each tree is built on a random subset of the training data
• Boostrapped version of data
– at each split point, only a random subset of predictors are considered
– Use “out-of-bag” hold out sample to estimate size of each tree
– prediction is simply majority vote of the trees ( or mean prediction of
the trees).
• Randomizing the variables used is the key
– Reduces correlation between models!
• Has the advantage of trees, with more robustness, and a
smoother decision rule.
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HTF Example p 589
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Breiman, Leo (2001). "Random Forests". Machine Learning 45 (1), 5-32
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Random Forests – How Big A Tree
• Breiman’s original
algorithm said: “to
keep bias low, trees
are to be grown to
maximum depth”
• However, empirical
evidence typically
shows that “stumps”
do best
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Ensembles – Main Points
• Averaging models together has been shown to be
effective for prediction
• Many weird names:
– See papers by Leo Breiman (e.g. “Bagging Predictors”,
Arcing the Edge”, and “Random Forests” for more detail
• Key points
– Models average well if they are uncorrelated
– Can inject randomness to insure uncorrelated models
– Averaging small models better than large ones
• Also, can give more insight into variables than
simple tree
– Variables that show up again and again must be good
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Visualizing Forests
• Data: Wisconsin Breast Cancer
– Courtesy S. Urbanek
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References
• Random Forests from Leo Breiman himself
• Breiman, Leo (2001). "Random Forests".
Machine Learning 45 (1), 5-32
• Hastie, Tibshirani, Friedman (HTF)
– Chapters 8,10,15,16
–
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