ICS 278: Data Mining Lectures 9 and 10: Classification Algorithms

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ICS 278 Data MiningLectures 9 and 10
Classification Algorithms

• Padhraic Smyth
• Department of Information and Computer Science
• University of California, Irvine

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Notation

• Variables X, C.. with values x, y (lower case)
• Vectors indicated by X
• Components of X indicated by Xj with values xj
• Matrix data set D with n rows and p columns
• jth column contains values for variable Xj
• ith row contains a vector of measurements on
  object i, indicated by x(i)
• The jth measurement value for the ith object is
  xj(i)
• Unknown parameter for a model q
• Can also use other Greek letters, like a, b, d, g
  ew
• Vector of parameters q

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Classification

• Predictive modeling predict Y given X
• Y is real-valued gt regression
• Y is categorical gt classification
• Often use C rather than Y to indicate the class
  variable
• Classification
• Many applications speech recognition, document
  classification, OCR, loan approval, face
  recognition, etc

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Classification v. Regression

• Similar in many ways
• both learn a mapping from X to C or Y
• Both sensitive to dimensionality of X
• Generalization to new data is important in both
• Test error versus model complexity
• Many models can be used for either classification
  or regression, e.g.,
• trees, neural networks
• Most important differences
• Categorical Y versus real-valued Y
• Different score functions
• E.g., classification error versus squared error

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Decision Region Terminlogy
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Probabilistic view of Classification

• Notation let there be K classes c1,..cK
• Class marginals p(ck) probability of class k
• Class-conditional probabilities p(
  x ck ) probability of x given ck , k 1,K
• Posterior class probabilities (by Bayes rule)
  p( ck x ) p( x ck ) p(ck) /
  p(x) , k 1,K
• where p(x)
  S p( x cj ) p(cj)
• In theory this is all we need.in practice
  this may not be best approach.

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Example of Probabilistic Classification
p( x c1 )
p( x c2 )
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Example of Probabilistic Classification
p( x c1 )
p( x c2 )
1
p( c1 x )
0.5
0
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Example of Probabilistic Classification
p( x c1 )
p( x c2 )
1
p( c1 x )
0.5
0
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Decision Regions and Bayes Error Rate
p( x c1 )
p( x c2 )
Class c2
Class c1
Class c2
Class c1
Class c2
Optimal decision regions regions where 1 class
is more likely Optimal decision regions ?
optimal decision boundaries
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Decision Regions and Bayes Error Rate
p( x c1 )
p( x c2 )
Class c2
Class c1
Class c2
Class c1
Class c2
Optimal decision regions regions where 1 class
is more likely Optimal decision regions ?
optimal decision boundaries Bayes error rate
fraction of examples misclassified by optimal
classifier
shaded area above (see equation
10.3 in text)
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Procedure for optimal Bayes classifier

• For each class learn a model p( x ck )
• E.g., each class is multivariate Gaussian with
  its own mean and covariance
• Use Bayes rule to obtain p( ck x )
• gt this yields the optimal decision
  regions/boundaries
• gt use these decision regions/boundaries for
  classification
• Correct in theory. but practical problems
  include
• How do we model p( x ck ) ?
• Even if we know the model for p( x ck ),
  modeling a distribution or density will be very
  difficult in high dimensions (e.g., p 100)
• Alternative approach model the decision
  boundaries directly

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3 categories of classifiers in general

• Generative (or class-conditional) classifiers
• Learn models for p( x ck ), use Bayes rule to
  find decision boundaries
• Examples naïve Bayes models, Gaussian
  classifiers
• Regression (or posterior class probabilities)
• Learn a model for p( ck x ) directly
• Example logistic regression (see lecture 5/6),
  neural networks
• Discriminative classifiers
• No probabilities
• Learn the decision boundaries directly
• Examples
• Linear boundaries perceptrons, linear SVMs
• Piecewise linear boundaries decision trees,
  nearest-neighbor classifiers
• Non-linear boundaries non-linear SVMs
• Note one can usually post-fit class
  probability estimates p( ck x ) to a
  discriminative classifier

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Which type of classifier is appropriate?

• Lets look at the score functions
• c(i) true class, c(x(i) q) class predicted
  by the classifier
• Class-mismatch loss functions
• S(q) 1/n Si Cost c(i), c(x(i) q)
  
• where cost(i, j) cost of misclassifying
  true class i as predicted class j
• e.g., cost(i,j) 0 if ij, 1 otherwise
  (misclassification error or 0-1 loss)
• and more generally cost(i,j) is a matrix of K
  x K losses (e.g., surgery, spam email, etc)
• Class-probability loss functions
• S(q) 1/n Si log p(c(i) x(i)
  q ) (log probability score)
• or S(q) 1/n Si c(i) p(c(i)
  x(i) q ) 2 (Brier score)

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Example classifying spam email

• 0-1 loss function
• Appropriate if we just want to maximize accuracy
• Asymmetric cost matrix
• Appropriate if missing non-spam emails is more
  costly than failing to detect spam emails
• Probability loss
• Appropriate if we wanted to rank all emails by
  p(spam email features), e.g., to allow the
  user to look at emails via a ranked list.
• In general dont solve a harder problem than you
  need to, or dont model aspects of the problem
  you dont need to (e.g., modeling p(xc)) -
  Vapnik, 1996.

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Examples of classifiers

• Generative/class-conditional/probabilistic, based
  on p( x ck ),
• Naïve Bayes (simple, but often effective in high
  dimensions)
• Parametric generative models, e.g., Gaussian (can
  be effective in low-dimensional problems leads
  to quadratic boundaries in general)
• Regression-based, p( ck x ) directly
• Logistic regression simple, linear in odds
  space
• Neural network non-linear extension of logistic,
  can be difficult to work with
• Discriminative models, focus on locating optimal
  decision boundaries
• Linear discriminants, perceptrons simple,
  sometimes effective
• Support vector machines generalization of linear
  discriminants, can be quite effective,
  computational complexity is an issue
• Nearest neighbor simple, can scale poorly in
  high dimensions
• Decision trees swiss army knife, often
  effective in high dimensionis

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Naïve Bayes Classifiers

• Generative probabilistic model with conditional
  independence assumption on p( x ck ), i.e.
  p( x ck ) P p( xj
  ck )
• Typically used with nominal variables
• Real-valued variables discretized to create
  nominal versions
• (alternative is to model each p( xj ck ) with a
  parametric model less widely used)
• Comments
• Simple to train (just estimate conditional
  probabilities for each feature-class pair)
• Often works surprisingly well in practice
• e.g., state of the art for text-classification,
  basis of many widely used spam filters
• Feature selection can be helpful, e.g.,
  information gain
• Note that even if CI assumptions are not met, it
  may still be able to approximate the optimal
  decision boundaries (seems to happen in practice)
• However. on most problems can usually be beaten
  with a more complex model (plus more work)

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Link between Logistic Regression and Naïve Bayes
Naïve Bayes
Logistic Regression
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Imbalanced Class Distributions

• Common in data mining to have one class be much
  less likely than the others
• e.g., 0.1 of examples are fraudulent or have a
  disease
• If we train a standard classifier on a random
  sample of data it is very difficult to beat the
  majority classifier in terms of accuracy
• Approaches
• Stratified sampling artificially create training
  data with 50 of each class being present, and
  then correct for this in prediction
• E.g., learn p(xc) on stratified data and use
  true p( c ) when predicting with a probabilistic
  model
• Use a different score function
• We are often interested in scoring/screening/ranki
  ng cases when using the model
• Thus, scores such as how many of the class of
  interest are ranked in the top 1 of predictions
  may be more relevant than overall accuracy (e.g.,
  in document retrieval)

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Ranking and Lift Curves

• Many problems where we are interested in ranking
  examples in terms of how likely they are to the
  positive class
• E.g., credit scoring, fraud detection, medical
  screening, document retrieval
• E.g., use classifier to rank N test examples
  according to p(cx) and then pick the top K,
  where K is much smaller than N
• Lift curve
• n number of true positives that appear in top
  K of ranked list
• r number of true positives that would appear if
  we ranked randomly
• n/r is the lift provided by the classifier for
  top K
• e.g., K 10, r 200, n 300, lift 1.5, or
  50 increase in lift
• Random ranking gives lift 1, or 0 increase in
  lift

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• Target variable response/no-response from
  mailing campaign
• Training and test sets each of size 250k
• Standard model had 80 variables variable
  selection reduced this to 7
• Note non-monotonicity in lower curve
  (undesirable)

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ROC plots

• Rank the N test examples by p(cx)
• or whatever real-number our classifier produces
  that indicates likelihood of belonging to class 1
• Let k number of examples classified in class 1,
  and m number in class 0, and km N
• For all possible thresholds for this ranked list
• count number of positives kt
• true positive rate kt /k
• count number of false alarms, mt
• false positive rate mt /m
• ROC plot plot of true positive rate kt v false
  positive rate mt

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ROC Example
Rank True Class True Positives False Positives
1 1 1 0
2 1 2 0
3 1 3 0
4 1 4 0
5 0 4 1
6 1 5 1
7 0 5 2
8 1 6 2
9 0 6 3
10 0 6 4

• N 10 examples,
• k 6 true class 1s,
• m 4 class 0s
• The first column is a possible ranking from a
  classifier

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ROC Plot

• Area under curve (AUC) often used as a metric to
  summarize ROC
• Online example at http//www.anaesthetist.com/mnm/
  stats/roc/

Diagonal line corresponds to random ranking
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Example Link Prediction in Coauthor Graphs O
Madadhain, Hutchins, Smyth, SIGKDD, 2005

• Binary classification problem
• Training data
• graph of coauthor links, 100k authors, 300k links
• data over several years
• Test data coauthor graph for same authors in a
  future year
• Classification problem
• predict if pair(A,B) will coauthor
• Training and test pairs selected in various ways
• Compared a variety of different classifiers and
  evaluation metrics
• Skewed class distribution
• No link present (class 0) in 93.8 of test
  examples
• Link present (class 1) in 6.2 of test examples

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Evaluation Metrics

• Classification error
• If p(linkA,B) gt 0.5, predict a link
• Brier Score
• S p(linkA,B I(A,B) 2
• ROC Area
• area under ROC plot (between 0 and 1)

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Link Prediction Evaluation
Classification Error
Baseline 6.2
Single Feature 6.2
Naïve Bayes 15.5
Logistic 6.1
Boosting 6.4
Averaged 6.2
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Link Prediction Evaluation
Classification Error ROC Area
Baseline 6.2 0.50
Single feature 6.2 0.54
Naïve Bayes 15.5 0.78
Logistic 6.1 0.80
Boosting 6.4 0.79
Averaged 6.2 0.80
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Link Prediction Evaluation
Classification Error ROC Area Brier Score
Baseline 6.2 0.50 100.0
Single feature 6.2 0.54 98.6
Naïve Bayes 15.5 0.78 211.7
Logistic 6.1 0.80 83.1
Boosting 6.4 0.79 83.4
Averaged 6.2 0.80 82.2
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Lift Curves for Different Models
Base Rate of links 6.2
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Interpretation of Ranking at Top of Ranked List

• Top 50 ranked candidates
• Averaged contains 44 true links
• Logistic contains 40 true links
• Baseline contains 3 true links
• Top 500 ranked candidates
• Averaged contains 300 true links
• Logistic contains 298 true links
• Baseline contains 31 true links

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Lift Curves for Different Models
Base Rate of links 0.2
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Calibration

• In addition to ranking we may be interested in
  how accurate our estimates of p(cx) are,
• i.e., if the model says p(cx) 0.9, how
  accurate is this number?
• Calibration
• a model is well-calibrated if its probabilistic
  predictions match real-world empirical
  frequencies
• i.e., if a classifier predicts p(cx) 0.9 for
  100 examples, then on average we would expect
  about 90 of these examples to belong to class c,
  and 10 not to.
• We can estimate calibration curves by binning a
  classifiers probabilistic predictions, and
  measuring how many

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Calibration in Probabilistic Prediction
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Linear Discriminants

• Discriminant -gt method for computing class
  decision boundaries
• Linear discriminant -gt linear decision boundaries
• Linear Discriminant Analysis (LDA)
• Earliest known classifier (1936, R.A. Fisher)
• See section 10.4 for math details
• Find a projection onto a vector such that means
  for each class (2 classes) are separated as much
  as possible (with variances taken into account
  appropriately)
• Reduces to a special case of parametric Gaussian
  classifier in certain situations
• Many subsequent variations on this basic theme
  (e.g., regularized LDA)
• Other linear discriminants
• Decision boundary (p-1) dimensional hyperplane
  in p dimensions
• Perceptron learning algorithms (pre-dated neural
  networks)
• Simple error correction based learning
  algorithms
• Linear SVMs use a sophisticated margin idea
  for selecting the hyperplane

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Nearest Neighbor Classifiers

• kNN select the k nearest neighbors to x from the
  training data and select the majority class from
  these neighbors
• k is a parameter
• Small k noisier estimates, Large k smoother
  estimates
• Best value of k often chosen by cross-validation
• Comments
• Virtually assumption free
• Gives piecewise linear boundaries (i.e.,
  non-linear overall)
• Interesting theoretical properties
  Bayes error lt error(kNN) lt 2 x Bayes error
  (asymptotically)
• Disadvantages
• Can scale poorly with dimensionality sensitive
  to distance metric
• Requires fast lookup at run-time to do
  classification with large n
• Does not provide any interpretable model

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Local Decision Boundaries
Boundary? Points that are equidistant between
points of class 1 and 2 Note locally the
boundary is (1) linear (because of Euclidean
distance) (2) halfway between the 2 class
points (3) at right angles to connector
1
2
Feature 2
1
2
2
?
1
Feature 1
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Finding the Decision Boundaries
1
2
Feature 2
1
2
2
?
1
Feature 1
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Finding the Decision Boundaries
1
2
Feature 2
1
2
2
?
1
Feature 1
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Finding the Decision Boundaries
1
2
Feature 2
1
2
2
?
1
Feature 1
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Overall Boundary Piecewise Linear
Decision Region for Class 1
Decision Region for Class 2
1
2
Feature 2
1
2
2
?
1
Feature 1
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Example Choosing k in kNN
(example from G. Ridgeway, 2003)
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Decision Tree Classifiers

• Widely used in practice
• Can handle both real-valued and nominal inputs
  (unusual)
• Good with high-dimensional data
• similar algorithms as used in constructing
  regression trees
• historically, developed both in statistics and
  computer science
• Statistics
• Breiman, Friedman, Olshen and Stone, CART, 1984
• Computer science
• Quinlan, ID3, C4.5 (1980s-1990s)

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Decision Tree Example
Debt
Income
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Decision Tree Example
Debt
Income gt t1
??
Income
t1
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Decision Tree Example
Debt
Income gt t1
t2
Debt gt t2
Income
t1
??
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Decision Tree Example
Debt
Income gt t1
t2
Debt gt t2
Income
t1
t3
Income gt t3
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Decision Tree Example
Debt
Income gt t1
t2
Debt gt t2
Income
t1
t3
Income gt t3
Note tree boundaries are piecewise linear and
axis-parallel
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Decision Tree Pseudocode
node tree-design(Data X,C) For i 1 to
d quality_variable(i) quality_score(Xi,
C) end node X_split, Threshold for
maxquality_variable Data_right, Data_left
split(Data, X_split, Threshold) if node
leaf? return(node) else node_right
tree-design(Data_right) node_left
tree-design(Data_left) end end
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Binary split selection criteria

• Q(t) N1Q1(t) N2Q2(t), where t is the
  threshold
• Let p1k be the proportion of class k points in
  region 1
• Error criterion for a branch
• Q1(t) 1 - p1k
• Gini index Q1(t) Sk p1k (1 -
  p1k)
• Cross-entropy Q1(t) Sk p1k
  log p1k
• Cross-entropy and Gini work better in practice
  than direct minimization of classification error
  at each node

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Computational Complexity for a Binary Tree

• At the root node, for each of p variables
• Sort all values, compute quality for each split
• O(pN log N) time for real-valued or ordinal
  variables
• Subsequent internal node operations each take
  O(N log N)
• e.g., balanced tree of depth K requires
• .. Homework 2 problem
• This assumes data are in main memory
• If data are on disk then repeated access of
  subsets at different nodes may be very slow
  (impossible to pre-index)
• Note time difference between retrieving data in
  RAM and data on disk may be O(103) or more.

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Splitting on a nominal attribute

• Nominal attribute with m values
• e.g., the name of a state or a city in marketing
  data
• 2m-1 possible subsets gt exhaustive search is
  O(2m-1)
• For small m, a simple approach is to branch on
  specific values
• But for large m this may not work well
• Neat trick for the 2-class problem
• For each predictor value calculate the proportion
  of class 1s
• Order the m values according to these proportions
• Now treat as an ordinal variable and select the
  best split (linear in m)
• This gives the optimal split for the Gini index,
  among all possible 2m-1 splits (Breiman et al,
  1984).

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How to Choose the Right-Sized Tree?
Predictive Error
Error on Test Data
Error on Training Data
Size of Decision Tree
Ideal Range for Tree Size
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Choosing a Good Tree for Prediction

• General idea
• grow a large tree
• prune it back to create a family of subtrees
• weakest link pruning
• score the subtrees and pick the best one
• Massive data sizes (e.g., n 100k data points)
• use training data set to fit a set of trees
• use a validation data set to score the subtrees
• Smaller data sizes (e.g., n 1k or less)
• use cross-validation
• use explicit penalty terms (e.g., Bayesian
  methods)

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Example Spam Email Classification

• Data Set (from the UCI Machine Learning Archive)
• 4601 email messages from 1999
• Manually labelled as spam (60), non-spam (40)
• 54 features percentage of words matching a
  specific word/character
• Business, address, internet, free, george, !, ,
  etc
• Average/longest/sum lengths of uninterrupted
  sequences of CAPS
• Error Rates (Hastie, Tibshirani, Friedman, 2001)
• Training 3056 emails, Testing 1536 emails
• Decision tree 8.7
• Logistic regression error 7.6
• Naïve Bayes 10 (typically)

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Treating Missing Data in Trees

• Missing values are common in practice
• Approaches to handing missing values
• During training
• Ignore rows with missing values (inefficient)
• During testing
• Send the example being classified down both
  branches and average predictions
• Replace missing values with an imputed value
  (can be suboptimal)
• Other approaches
• Treat missing as a unique value (useful if
  missing values are correlated with the class)
• Surrogate splits method
• Search for and store surrogate variables/splits
  during training

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Other Issues with Classification Trees

• Why use binary splits?
• Multiway splits can be used, but cause
  fragmentation
• Linear combination splits?
• can produces small improvements
• optimization is much more difficult (need weights
  and split point)
• Trees are much less interpretable
• Model instability
• A small change in the data can lead to a
  completely different tree
• Model averaging techniques (like bagging) can be
  useful
• Tree bias
• Poor at approximating non-axis-parallel
  boundaries
• Producing rule sets from tree models (e.g., c5.0)

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Why Trees are widely used in Practice

• Can handle high dimensional data
• builds a model using 1 dimension at time
• Can handle any type of input variables
• categorical, real-valued, etc
• most other methods require data of a single type
  (e.g., only real-valued)
• Trees are (somewhat) interpretable
• domain expert can read off the trees logic
• Tree algorithms are relatively easy to code and
  test

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Limitations of Trees

• Representational Bias
• classification piecewise linear boundaries,
  parallel to axes
• regression piecewise constant surfaces
• Trees do not scale well to massive data sets
  (e.g., N in millions)
• repeated (unpredictable) access of subsets of the
  data
• e.g., compare to linear scanning
• High Variance
• trees can be unstable as a function of the
  sample
• e.g., small change in the data -gt completely
  different tree
• causes two problems
• 1. High variance contributes to prediction error
• 2. High variance reduces interpretability
• Trees are good candidates for model combining
• Often used with boosting and bagging

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Decision Trees are not stable
Moving just one example slightly may lead to
quite different trees and space partition! Lack
of stability against small perturbation of data.
Figure from Duda, Hart Stork, Chap. 8
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Example of Tree Instability 2 trees fit to 2
splits of data, from G. Ridgeway, 2003
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Model Averaging

• Can average over parameters and models
• E.g., weighted linear combination of predictions
  from multiple models
• y S wk yk
• Why? Any predictions from a point estimate of
  parameters or a single model has only a small
  chance of the being the best
• Averaging makes our predictions more stable and
  less sensitive to random variations in a
  particular data set (good for less stable models
  like trees)

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Model Averaging

• Model averaging flavors
• Fully Bayesian average over uncertainty in
  parameters and models
• empirical Bayesian learn weights over multiple
  models
• E.g., stacking and bagging
• Build multiple simple models in a systematic way
  and combine them, e.g.,
• Boosting will say more about this in later
  lectures
• Random forests stochastically perturb the data,
  learn multiple trees, and then combine for
  prediction

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Bagging for Combining Classifiers

• Training data sets of size N
• Generate B bootstrap sampled data sets of size
  N
• Bootstrap sample sample with replacement
• e.g. B 100
• 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
• For prediction, combine the predictions from the
  B models
• E.g., for classification p(c x) fraction of B
  models that predict c
• Plus generally improves accuracy on models such
  as trees
• Negative lose interpretability
• Related techniques random forests, boosting.

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green majority vote purple averaging the
probabilities
From Hastie, Tibshirani, and Friedman, 2001
68
Illustration of Boosting Color of points class
label Diameter of points weight at each
iteration Dashed line single stage classifier.
Green line combined, boosted classifier Dotted
blue in last two bagging (from G. Rätsch, Phd
thesis, 2001)
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Support Vector Machines

• Support vector machines
• Use a different loss function, the margin
• Results in convex optimization problem, solvable
  by quadratic programming
• Decision boundary represented by examples in
  training data
• Linear version clever placement of the
  hyperplane
• Non-linear version kernel trick for
  high-dimensional problems
• Computational complexity can be O(N3) without
  speedups

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Experiments by Komarek and Moore, 2005
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Accuracies and Training Time Komarek and
Moore, 2005
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Accuracies and Training Time Komarek and
Moore, 2005
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Summary on Classifiers

• Simple models (but can be effective)
• Logistic regression
• Naïve Bayes
• K nearest-neighbors
• Decision trees
• Good for high-dimensional problems with different
  data types
• State of the art
• Support vector machines
• Boosted trees (e.g., boosting with naïve Bayes or
  with decision stumps)
• Many tradeoffs in interpretability, score
  functions, etc

74
Decision Tree Classifiers
Classification
Task
Decision boundaries hierarchy of axis-parallel
Representation
Cross-validated error
Score Function
Greedy search in tree space
Search/Optimization
Data Management
None specified
Models, Parameters
Tree
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Naïve Bayes Classifier
Classification
Task
Conditional independence probability model
Representation
Score Function
Likelihood
Closed form probability estimates
Search/Optimization
Data Management
None specified
Models, Parameters
Conditional probability tables
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Logistic Regression
Task
Classification
Log-odds(C) linear function of Xs
Representation
Score Function
Log-likelihood
Search/Optimization
Iterative (Newton) method
Data Management
None specified
Models, Parameters
Logistic weights
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Nearest Neighbor Classifier
Task
Classification
Representation
Memory-based
Cross-validated error (for selecting k)
Score Function
Search/Optimization
None
Data Management
None specified
Models, Parameters
None
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Support Vector Machines
Task
Classification
Representation
Hyperplanes
Score Function
Margin
Convex optimization (quadratic programming)
Search/Optimization
Data Management
None specified
Models, Parameters
None
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Neural Networks
Task
Regression
Representation
Y nonlin function of Xs
Score Function
Least-squares
Search/Optimization
Gradient descent
Data Management
None specified
Models, Parameters
Network weights
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Multivariate Linear Regression
Task
Regression
Y Weighted linear sum of Xs
Representation
Score Function
Least-squares
Search/Optimization
Linear algebra
Data Management
None specified
Models, Parameters
Regression coefficients
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Autoregressive Time Series Models
Task
Time Series Regression
X Weighted linear sum of earlier Xs
Representation
Score Function
Least-squares
Search/Optimization
Linear algebra
Data Management
None specified
Models, Parameters
Regression coefficients
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Software (same as for Regression)

• MATLAB
• Many free toolboxes on the Web for regression
  and prediction
• e.g., see http//lib.stat.cmu.edu/matlab/ and
  in particular the CompStats toolbox
• R
• General purpose statistical computing environment
  (successor to S)
• Free (!)
• Widely used by statisticians, has a huge library
  of functions and visualization tools
• Commercial tools
• SAS, other statistical packages
• Data mining packages
• Often are not progammable offer a fixed menu of
  items

83
Reading

• For this class Chapter 10
• Covers both general concepts in classification
  and a broad range of classifiers
• Suggested background reading for further
  information
• Elements of Statistical Learning,
• T. Hastie, R. Tibshirani, and J. Friedman,
  Springer Verlag, 2001
• Learning from Kernels,
• B Schoelkopf and A. Smola, MIT Press, 2003.
• Classification Trees,
• Breiman, Friedman, Olshen, and Stone, Wadsworth
  Press, 1984.

84
Backup Slides (not used)