辅导案例-CSCI 4622

Machine Learning


CSCI 4622
Fall 2019

Prof. Claire Monteleoni



Today: Lecture 10

•  Introduction to Ensemble Methods
– Random Forests
– Voted Perceptron
– Boosting (if time)

With slide credits: S. Dasgupta, T. Jaakkola, J. Shotton, C. Tan, and A. Torralba
Review: (Binary) Decision Trees
class c
split nodes
leaf nodes
v

<
<

Credit: Shotton, ICCV 09 Tutorial
Distribution
over class labels
Speeding up Decision Tree Learning
x
y
It is cumbersome to test
all possible splits

Try several random splits

Keep the split that best
separates data
•  Reduces uncertainty
Recurse
Animation: Shotton, ICCV 09 Tutorial
x
y
Speeding up Decision Tree Learning
Shotton, ICCV 09 Tutorial
It is cumbersome to test
all possible splits

Try several random splits

Keep the split that best
separates data
•  Reduces uncertainty
Recurse
x
y
Speeding up Decision Tree Learning
Shotton, ICCV 09 Tutorial
It is cumbersome to test
all possible splits

Try several random splits

Keep the split that best
separates data
•  Reduces uncertainty
Recurse
x
y
Speeding up Decision Tree Learning
Shotton, ICCV 09 Tutorial
Random Decision Tree

It is cumbersome to test
all possible splits

Try several random splits

Keep the split that best
separates data
•  Reduces uncertainty
Recurse
Random Decision Trees
•  How many features and thresholds to try?
–  just one: “extremely randomized” [Geurts et al. 06]
–  few: fast training, may under-fit
–  many: slower training, may over-fit
•  When to stop growing the tree?
–  maximum depth
–  minimum entropy gain
–  threshold changes in class distribution
–  pruning
•  A forest is an ensemble of several decision trees

Classification:
Decision Forests
……
tree t1 tree tT
class c
class c
split nodes
leaf nodes
v v
Shotton, ICCV 09 Tutorial
Learning a Forest
•  Divide training examples into T subsets St
–  improves generalization
–  reduces memory requirements & training time

•  Train each decision tree, t, on subset St
–  same decision tree learning as before
•  Easy to parallelize

An ensemble classifier combines a set of weak “base”
classifiers into a “strong” ensemble classifier.
•  “boosted” performance
•  more robust against overfitting
•  Decision Forests, Random Forests [Breiman ‘01], Bagging
•  Voted-Perceptron
•  Boosting
•  Learning with expert advice
•  ….

Ensemble methods
Perceptron: nonseparable data
What if data is not linearly separable?
In this case: almost linearly separable… how will the
perceptron perform?
Batch perceptron
Batch algorithm:

w = 0
while some (xi,yi) is misclassified: w = w + yi xi


Nonseparable data: this algorithm will never converge.
How can this be fixed?

Dream: somehow find the separator that misclassifies the
fewest points… but this is NP-hard (in fact, even NP-
hard to approximately solve).
Fixing the batch perceptron
Idea 1: only go through the data once, or a fixed number of times, K

w = 0
for k = 1 to K
for i = 1 to m
if (xi,yi) is misclassified: w = w + yi xi
At least this stops!

Problem: the final w might not be good.
Eg. right before terminating, the algorithm might perform an update on
an outlier!
Voted-perceptron
Idea 2: keep around intermediate hypotheses, and have them
“vote” [Freund and Schapire, 1998]

n = 1
w1 = 0 c1 = 0 for k = 1 to K
for i = 1 to m
if (xi,yi) is misclassified: wn+1 = wn + yi xi cn+1 = 1 n = n + 1
else
cn = cn + 1
At the end, a collection of linear separators w0, w1, w2, …, along
with survival times: cn = amount of time that wn survived.
Voted-perceptron
Idea 2: keep around intermediate hypotheses, and have them
“vote” [Freund and Schapire, 1998]

At the end, a collection of linear separators w0, w1, w2, …, along
with survival times: cn = amount of time that wn survived.

This cn is a good measure of the reliability (or confidence) of wn.

To classify a test point x, use a weighted majority vote:


Voted-perceptron
Problem: may need to keep around a lot of wn vectors.

Solutions:

(i)  Find “representatives” among the w vectors.
(ii)  Alternative prediction rule:
Just keep track of a running average, wavg
100 rounds, 1595 updates (5 errors)
Final hypothesis: makes 5 errors for voting
IRIS: features 3 and 4; goal: separate + from o/x
Interesting questions
Modify the (voted) perceptron algorithm to:

[1] Find a linear separator with large margin









[2] “Give up” on troublesome points after a while
Voting margin and generalization
• If we can obtain a large (positive) voting margin



across the training examples, we will have better
generalization guarantees (discussed later)
fraction of points
with positive margin
fraction of points
with margin >0.05
fraction of points
with margin >0.1
iteration
•  A simple algorithm for learning robust ensemble
classifiers
–  Freund & Shapire, 1995
–  Friedman, Hastie, Tibshhirani, 1998

•  Easy to implement, no external optimization tools
needed.

Boosting
•  Defines a classifier using an additive model:

Boosting
Strong
classifier
Weak classifier
Weight
Data point
•  Defines a classifier using an additive model:

•  We need to define a family of weak classifiers
–  E.g. linear classifiers, decision trees, or even decision stumps
(threshold on one axis-parallel dimension)
Boosting
Strong
classifier
Weak classifier
Weight
Data point
Each data point has
a class label:

- we initialize all wt =1
and a weight, wt.
+1 ( )
-1 ( )
yt =
Boosting
•  Run sequentially on a batch of n data points


xt=1
xt=2
xt
Example using linear separators
Weak learners from the family of lines
This linear separator has error rate 50%
and a weight:
Torralba, ICCV 05 Short Course
Each data point has
a class label:
+1 ( )
-1 ( )
yt =
wt =1
Example using linear separators
This one seems to be the best, call it f1
and a weight:
This is a ‘weak classifier’: Its error rate is slightly less than 50%.
Torralba, ICCV 05 Short Course
wt =1
Each data point has
a class label:
+1 ( )
-1 ( )
yt =
Example using linear separators
- Re-weight the points such that the previous weak classifier now has 50% error
- Iterate: find a weak classifier for this new problem
We update the weights:
Torralba, ICCV 05 Short Course
Each data point xi has
a class label:
+1 ( )
-1 ( )
yi=
wt wt exp{-yt fm(xt)}
Example using linear separators
We update the weights:
Torralba, ICCV 05 Short Course
- Re-weight the points such that the previous weak classifier now has 50% error
- Iterate: find a weak classifier for this new problem
Each data point xi has
a class label:
+1 ( )
-1 ( )
yi=
wt wt exp{-yt fm(xt)}
Example using linear separators
We update the weights:
Torralba, ICCV 05 Short Course
- Re-weight the points such that the previous weak classifier now has 50% error
- Iterate: find a weak classifier for this new problem
Each data point xi has
a class label:
+1 ( )
-1 ( )
yi=
wt wt exp{-yt fm(xt)}
Example using linear separators
We update the weights:
Torralba, ICCV 05 Short Course
- Re-weight the points such that the previous weak classifier now has 50% error
- Iterate: find a weak classifier for this new problem
Each data point xi has
a class label:
+1 ( )
-1 ( )
yi=
wt wt exp{-yt fm(xt)}
Example using linear separators
The strong (non-linear) ensemble classifier is built as a weighted combination
of all the weak (linear) classifiers.
f1 f2
f3
f4
Torralba, ICCV 05 Short Course
Boosting
•  AdaBoost (Freund and Shapire, 1995)
•  Real AdaBoost (Friedman et al, 1998)
•  LogitBoost (Friedman et al, 1998)
•  Gentle AdaBoost (Friedman et al, 1998)
•  BrownBoosting (Freund, 2000)
•  FloatBoost (Li et al, 2002)
•  …
Mostly differ in choice of loss function and how
it is minimized. Torralba, ICCV 05 Short Course
Loss functions: motivation
• Want a smooth upper bound on 0-1 training error.
training loss
0-1 training error
iteration
Loss functions
-1.5 -1 -0.5 0 0.5 1 1.5 2 0
0.5
1
1.5
2
2.5
3
3.5
4 Squared error
Exponential loss
yF(x) = margin
Misclassification error
Loss
Squared error
Exponential loss
Torralba, ICCV 05 Short Course
Boosting
Sequential procedure. At each step we add
For more details: Friedman, Hastie, Tibshirani. “Additive Logistic Regression: a Statistical View of Boosting” (1998)
to minimize the residual loss



input Desired output Parameters
weak classifier
How to set the ensemble weights?
•  Prediction on a new data point x is typically of the form:
•  How to set the values?
•  Depends on the algorithm. E.g. in AdaBoost:
•  Where is the training error of on the (currently)
weighted data set.
F (x) =
kX
m=1
↵mfm(x)
↵m
↵m =
1
2
ln
1 ✏m
✏m
fm✏m
Boosting example
Boosting example
Boosting example
Boosting example
Boosting example
Bias-Variance Error Decomposition
Assume the data (x,y) is drawn i.i.d. from D, s.t.:
where:

Then, for any data point (x0,y0),



Irreducible error (can’t be changed by choice of h)
where:


NOTE: All expectations are w.r.t. the random training set h is learned from, NOT x0.






y = f(x) + ✏
E[✏] = 0
Var(✏) = 2
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Var[h(x0)] = E[h(x0)
2] E[h(x0)]2
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= 2 +Var[h(x0)] + (Bias[h(x0)])
2
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Understanding boosting
• There are four different kinds of errors in the boosting
procedure that we can try to understand
-  weighted error that the base learner achieves at each
iteration
-  weighted error of the base learner relative to just updated
weights (i.e., trying the same base learner again)
-  training error of the ensemble as a function of the number of
boosting iterations
-  generalization error of the ensemble
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