lectures 15,16 – additive models, trees, and related methods rice ece697 farinaz koushanfar fall...

Lectures 15,16 – Additive Models, Trees, and Related Methods

Rice ECE697

Farinaz Koushanfar

Fall 2006

Summary

• Generalized Additive Models

• Tree-Based Methods

• PRIM – Bump Hunting

• Mutlivariate Adaptive Regression Splines (MARS)

• Missing Data

Additive Models

• In real life, effects are nonlinear

•

Note: Some slides are borrowed from Tibshirani

Examples

The Price for Additivity

Data from a study of Diabetic children, Predicting log C-peptide(a blood measurement)

Generalized Additive Models (GAM)Two-class Logistic Regression

Other Examples

Fitting Additive Models

• Given observations xi,yi, a criterion like the penalized sum of squares can be specified for this problem, where ’s are tuning parameters

p

1jjj)X(fY The mean of error term is zero!

N

1i

p

1jj

2

jjj

2p

1jijji

p1

dt)t("f})x(fy{

)f,...,f,(PRSS

Fitting Additive Models

The Backfitting Algorithm for Additive Models

• Initialize:

• Cycle: j=1,2,…,p,1,2,…,p,1,…

• Until the functions fj change less than a prespecified threshold

j,i,0f̂;yN

1j

N

1ii

]})x(f̂y[{Sf̂ N

1jk

ikkijj

N

1iijjjj)x(f̂

N

1f̂f̂

Fitting Additive Models (Cont’d)

Example: Penalized Least square

Example: Fitting GAM for Logistic Regression (Newton-Raphson Algorithm)

Example: Predicting Email Spam

• Data from 4601 mail messages, spam=1, email=0, filter trained for each user separately

• Goal: predict whether an email is spam (junk mail) or good

• Input features: relative frequencies in a message of 57 of the commonly occurring words and punctuation marks in all training set

• Not all errors are equal; we want to avoid filtering out good email, while letting spam get through is not desirable but less serious in its consequences

Predictors

Details

Some Important Features

Results

• Test data confusion matrix for the additive logistic regression model fit to the spam training data

• The overall test error rate is 5.3%

Summary of Additive Logistic Fit• Significant predictors from the additive model fit to the spam

training data. The coefficients represent the linear part of f^j,

along with their standard errors and Z-score. • The nonlinear p-value represents a test of nonlinearity of f^

j

Example: Plots for Spam Analysis

Figure 9.1. Spam analysis: estimated functions for significant predictors. The rug plot along the bottom of each frame indicates the observed values of the corresponding predictor. For many predictors, the nonlinearity picks up the discontinuity at zero.

In Summary

• Additive models are a useful extension to linear models, making them more flexible

• The backfitting procedure is simple and modular

• Limitations for large data mining applications

• Backfitting fits all predictors, which is not desirable when a large number are available

Tree-Based Methods

Node Impurity Measures

Results for Spam Example

Pruned tree for the Spam Example

Classification Rules Fit to the Spam Data

PRIM-Bump Hunting

Number of Observations in a Box

Basis Functions

MARS Forward Modeling Procedure

Multiplication of Basis Functions

MARS on Spam Example