Regression trees and regression graphs: Efficient estimators for Generalized Additive Models

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Regression trees and regression graphs: Efficient estimators for Generalized Additive Models

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Regression trees and regression graphs: Efficient estimators for Generalized Additive Models

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Regression trees and regression graphs:Efficient estimators for Generalized Additive Models

Adam Tauman Kalai

TTI-Chicago

- Generalized Additive Models (GAM)
- Computationally efficient regression
- Model
Thm: Regression graph algorithm efficiently learns GAMs

- Model
- Regression tree algorithm
- Regression graph algorithm
Correlation boosting

- [Valiant] [Kearns&Schapire]

New

[Mansour&McAllester]

New

- e.g., Generalized linear models
- u( w¢x ), monotonic u
- linear/logistic models

- e.g., f(x) = e–||x||2 = e–x(1)2–x(2)2…–x(d)2

Dist. over X£Y = Rd£R

f(x) = E[y|x] = u(f1(x(1))+f2(x(2))+…+fd(x(d)))

monotonic u: R!R, arbitrary fi: R!R

Non-Hodgkin’s Lymphoma International Prognostics Index

[NEJM ‘93]

Risk Factors

age>60, # sites>1, perf. status>1, LDH>normal, stage>2

Setup

X £Y

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regression

algorithm

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“training error”

(h,train) = i(h(xi)-y)2

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h: X! [0,1]

“true error”

(h) = E[(h(x)-y)2]

X = RdY = [0,1]

training sample:

(x1,y1),…,(xn,yn)

X £ [0,1]

h: X! [0,1]

Family of

target functions

Definition: A efficiently learns F:

f(x) = E[y|x] 2F,

8

with probability 1-,

>0

E[(h(x)-y)2] · E[(f(x)-y)2]+(term)/nc

n examples

true error (h)

poly(|f|,1/)

Learning

Algorithm

A

A’s runtime must be poly(n,|f|)

- Generalized Additive Models (GAM)
- Computationally efficient regression
- Model
Thm: Regression graph algorithm efficiently learns GAMs

- Model
- Regression tree algorithm
- Regression graph algorithm
Correlation boosting

- [Valiant] [Kearns&Schapire]

New

[Mansour&McAllester]

New

New

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Regression

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Learner

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h: Rd ![0,1]

n samples 2 X £ [0,1]

X µRd

Thm:reg. graph learner efficiently learns GAMs

- 8dist over X£Y with E[y|x] = f(x) 2 GAM
- E[(h(x)-y)2] · E[(f(x)-y)2] + O(LV log(dn/))
- runtime = poly(n,d)

8 with probability 1-,

n1/7

New

- f(x) = u(i fi(x(i)))
- u: R!R, monotonic, L-Lipschitz (L=max |u’(z)|)
- fi: R!R, bounded total variationV = i s |fi’(z)|dz

Thm:reg. graph learner efficiently learns GAMs

- 8dist over X£Y with E[y|x]=f(x) 2 GAM
- E[(h(x)-y)2] · E[(f(x)-y)2] + O(LV log(dn/))
- runtime = poly(n,d)

n1/7

New

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Learner

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h: Rd ![0,1]

n samples 2 X £ [0,1]

X µRd

Thm:reg. tree learner inefficiently learns GAMs

- 8dist over X£Y with E[y|x]=f(x) 2 GAM
- E[(h(x)-y)2] · E[(f(x)-y)2] + O(LV)
- runtime = poly(n,d)

(

)

1/4

log(d)

log(n)

- Regression tree RT: Rd! [0,1]
- Training sample (x1,y1),(x2,y2),…,(xn,yn) 2Rd£ [0,1]

(x1,y1),

(x2,y2),

…

avg(y1,y2,…,yn)

- Regression tree RT: Rd! [0,1]
- Training sample (x1,y1),(x2,y2),…,(xn,yn) 2Rd£ [0,1]

x(j) ¸ ?

(xi,yi): x(j) <

(xi,yi): x(j) ¸

avg(yi: xi(j)<)

avg(yi: xi(j)¸)

- Regression tree RT: Rd! [0,1]
- Training sample (x1,y1),(x2,y2),…,(xn,yn) 2Rd£ [0,1]

x(j) ¸ ?

(xi,yi): x(j) <

x(j’) ¸’ ?

avg(yi: xi(j)<)

(xi,yi): x(j) ¸

andx(j’)< ’

(xi,yi): x(j) ¸

andx(j’) ¸’

avg(yi: x(j)¸Æx(j’)¸’)

avg(yi: x(j)¸Æx(j’)<’)

- n = amount of training data
- Put all data into one leaf
- Repeat until size(RT)=n/log2(n):
- Greedily choose leaf and split x(j) · to minimize (RT,train) = (RT(xi)-yi)2/n
- Divide data in split node into two new leaves

Equivalent to “Gini”

Regression Graph Algorithm [Mansour&McAllester]

- Regression graph RG: Rd! [0,1]
- Training sample (x1,y1),(x2,y2),…,(xn,yn) 2Rd£ [0,1]

x(j) ¸ ?

x(j’’) ¸’’ ?

x(j’) ¸’ ?

(xi,yi): x(j) <

andx(j’’)< ’’

(xi,yi): x(j) <

andx(j’’) ¸’’

(xi,yi): x(j) ¸

andx(j’)< ’

(xi,yi): x(j) ¸

andx(j’) ¸’

avg(yi: x(j)¸Æx(j’)¸’)

avg(yi: x(j)<Æx(j’’)<’’)

avg(yi: x(j)¸Æx(j’)<’)

avg(yi: x(j)<Æx(j’’)¸’’)

Regression Graph Algorithm [Mansour&McAllester]

- Regression graph RG: Rd! [0,1]
- Training sample (x1,y1),(x2,y2),…,(xn,yn) 2Rd£ [0,1]

x(j) ¸ ?

x(j’’) ¸’’ ?

x(j’) ¸’ ?

(xi,yi): x(j) <

andx(j’’)< ’’

(xi,yi): x(j) < andx(j’’) ¸’’

or x(j) ¸ and x(j’) < ’

(xi,yi): x(j) ¸

andx(j’) ¸’

avg(yi: x(j)¸Æx(j’)¸’)

avg(yi: x(j)<Æx(j’)<’)

avg(yi: (x(j)<Æx(j’’)¸’’)Ç(x(j)¸Æx(j’)<’))

- Put all n training data into one leaf
- Repeat until size(RG)=n3/7:
- Split: greedily choose leaf and split x(j) · to minimize (RG,train) = (RG(xi)-yi)2/n
- Divide data in split node into two new leaves
- Let be the decrease in (RG,train) from this split

- Merge(s):
- Greedily choose two leaves whose merger increases (RG,train) as little as possible
- Repeat merging while total increase in (RG,train) from merges is ·/2

- Split: greedily choose leaf and split x(j) · to minimize (RG,train) = (RG(xi)-yi)2/n

- Uniform generalization bound for any n:
- Existence of a correlated split:There always exists a split I(x(i) ·) s.t.,

regression graph R

probability over training sets (x1,y1),…,(xn,yn)

- X = {0,1}d, f(x) = (x(1)+x(2)+…+x(d))/d, uniform
- Size(RT) ¼ exp(Size(RG)c), e.g. d=4:

x(1)>½

x(1)>½

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- Incremental learning
- Suppose you find something of positive correlation with y, then reg. graphs make progress
- “Weak regression” implies strong regression, i.e. small correlations can efficiently be combined to get correlation near 1 (error near 0)
- Generalizes binary classification boosting[Kearns&Valiant, Schapire, Mansour&McAllester,…]

- Generalized additive models are very general
- Regression graphs, i.e., regression trees with merging, provably estimate GAMs using polynomial data and runtime
- Regression boosting generalizes binary classification boosting
- Future work
- Improve algorithm/analysis
- Room for interesting work in statistics Å computational learning theory