# Estimating by MikeJenny

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```									Learning and Making Decisions
When Costs and Probabilities are
Both Unknown
by B. Zadrozny and C. Elkan
Contents
•   Introduce the Problem
•   Previous work
•   Direct Cost Sensitive Decision Making
•   The Dataset
•   Estimating Class Membership Probabilities
•   Estimating Costs
•   Results and Conclusions
Introduction
• Costs/Benefits are the values assigned to
classification decisions.
• Cost are often different for different
examples
• Often we are interested in the rare class in
cost-sensitive learning
• Hence the problem of unbalanced data
Cost Sensitive Decisions
• Each training and test example has associated
cost
• General optimal prediction
arg min  P( j | x)C (i, j , x)
i     j
• Methods differ w.r.t. P( j | x) and C (i, j , x)
• Previous literature has assumed cost are known
in advance and independent of examples.

C (i, j, x)  C (i, j, y) x, y
MetaCost
• Estimation of P( j | x)
– Estimated in training only.
• Estimation of C (i, j )
– Example independent
• Training changes labelling to its optimum
• Learns a classifier to predict labeling of test
examples.
Direct Cost-Sensitive-Decision-
Making
• Estimation of P( j | x)
– Average of Naïve Bayes and Decision Trees .
– Estimated on training and test sets.
• Estimation of C (i, j , x)
– Multiple Linear Regression.
– Unbiased estimate using Heckman procedure
– Example dependant.
• Evaluation
– Evaluate against MetaCost and KDD competition
results.
– Using a large and difficult dataset, KDD.
MetaCost Implementation
• For evaluation MetaCost is adapted
– Probability class estimates found by simple methods
using decision trees
– Cost are made example dependant during training
• Adapted MetaCost vs. DCSDM
– DCSDM uses two models on test example MetaCost
one.
– Estimation P( j | x) was made in both training and test
examples in DCSDM.
The Data Mining Task
• Data on persons who have donated in
the past to a certain charity, KDD '98
competition
– Non-donor and donor labelling based on
last campaign
• The task is to choose which donors to
ask for new donations
• Training/Test set
– 95,412 records, labelled donors or non-
donors and donation amount
– 96,367 unlabelled records from same
donation campaign
Data Mining Task cont.
• Cost of soliciting \$0.68
• Donations range from \$1-200
• 5% donors and 95% non-donors
– Very low response rate and varying
donations make hard to beat soliciting to
everyone.
• The dataset set is hard
– Already been filtered to be a reasonable set
of prospects
– The task is to improve upon the unknown
method that produced the set
Applied DCSDM
• In KDD we will change C(i,j,x) to B(i,j,x)
– Costs become benefits

Actual             Actual
Non-donor          Donor
Predict            0                  0
Non-donor
Predict Donor      -0.68              y(x)-0.68

• B(1,1,x) is example dependant
– Replaced by a constant by previous literature
Optimal policy
• The expected benefit of not soliciting, i = 0
P( j  0 | x) B(0,0, x)  P( j  1 | x) B(0,1, x)  0
• Expected benefit of soliciting, i = 1
P( j  0 | x) B(1,0, x)  P( j  1 | x) B(1,1, x)
 (1  P( j  1 | x))(0.68)  P( j  1 | x)( y ( x)  0.68)
 P( j  1 | x) y ( x)  0.68

• Optimal policy: P( j  1 | x) y ( x)  0.68  0
P( j | x)
• Optimal decisions require P ( j | x)
• Class sizes may be highly unbalanced
• Two methods proposed
– Decision Trees - Smoothing Curtailment
– Naïve Bayes - Binning
Problems w. Decision Trees
• Decision trees assign as a score to each leaf
the raw training frequency of that leaf.
k
p
n
• High Bias
– Decision trees growing methods try to make leaves
Homogeneous. p’s tend to be over or under
estimates
• High Variance
– When n is small p not to be trusted.
Smoothing
• Pruning is no good for us.
• To make the estimates less extreme lets replace:

k       k  b.m
p   p' 
n        nm
• b – base rate, m – heuristic value (smoothing strength)
• Effect
– where k, n small p’ essentially just base rate.
– If k, n larger then p’ is ‘combination’ of base rate and original
score
Smoothed Scores
Curtailment
• What if the leaves have enough training
examples to be statistically reliable?
– Then smoothing seems to be unnecessary.
• Curtailment searches through the tree and
removes nodes where n < v.
– V chosen either through cross-validation, or a
heuristic, like b.v = 10.
Curtailed Tree
Curtailed Scores
Naïve Bayes Classifiers
• Naïve Bayes
– Assumes that within any class the attribute values are
independent variables.
• This assumption gives inaccurate probability
estimates
• But, attributes tend to be positively correlated so
naïve Bayes estimates tend to be too extreme,
i.e. close to zero or one.
• So, they do rank examples well:
if n( x)  n( y) then P( j  1 | x)  P( j  1 | y)
Calibrating Naïve Bayes Scores
• The Histogram method:
– Sort training examples by n.b. scores
– Divide sorted set into b subsets of equal size,
called bins
– For each bin compute lower and upper
boundary n.b. scores
• Given a new data point x
– Calculate n(x ) and find the associated bin
– Let P( j | x) = fraction of positive training
examples in that bin
Averaging Probability Estimates
• If probability estimates are partially uncorrelated
then it follows that averaging these estimates will
reduce their variance.
• Assuming all estimates have the same variance
the average estimate will have a variance given
by:             2  1   ( N  1) 2
                
N
2   The individual classifier variances

N   The number of classifiers
    The correlation factor among all classifiers
Estimating Donation Amount
• Solicit the person based on policy.
• Policy

P( j  1 | x) y ( x)  0.68  0

y (x )   is estimated donation amount
Cost and Probability
• Good Decisions
– Estimating Cost well is more important than
estimating probabilities.
• Why?
– Relative variation of cost across different examples is
much greater than the relative variation of
probabilities
• Probability
– Estimating Donation probability is difficult.
• Estimating donation amount are easier because
past amount are excellent predictor of future
amounts.
Training and Test data
• Two random process
– Donate or not to.
– How much to donate?
Donation Amount.
Donor        Non donor
Training data   Known         -
Test data       unknown      unknown

• Method used for estimating donation amount is
called as Multiple Linear regression (MLR).
Multiple Linear Regression
Two attributes are used
– lastgift : dollar amount of most recent gift.
– ampergift : average gift amount in response to the last
22 promotions
• Linear Regression equation is used to estimate
donation amount.
• 46 of 4843 donations recorded have donation
amount more than \$50.
• Donors that have donated at most \$50 are used
as input for linear regression.
Problem of Sample Selection
Bias
• Reasoning outside your learning space.
• Donation Amount
Donor                Non donor
Training data      Known                -
Test data          unknown              unknown

• Estimating Donation Amount
– Any donation estimator is learned on the basis of
people who actually donated.
– This estimator is applied to different population
consisting of donors and non-donors.
Donation Amount and Probability
Estimates are Negatively
Correlated
Solution to Sample Selection
Bias
• Heckman’s procedure
– Estimate conditional probability p( j=1 | x) using linear
probit model.
– Estimate y(x) on training dataset for which j (x) = 1
by including a transformation for each x using the
estimated values of conditional probability.
• Their own procedure
– conditional probability is learned using decision tree
or Naïve bayes classifier.
– These probability estimates are added as additional
attributes by estimating y(x).
Experimental Results
Direct cost sensitive Decision Making

Meta cost
Experimental Results
Interpretation
• With Heckman
– profits on test set increases by \$484, in all probability
estimation methods.
– Systematic improvement indicates that Heckman’s
procedure solves the problem of Sample Selection
Bias
• Meta cost
– Best result of Meta cost is \$14113.
– Best result of Direct cost sensitive method is \$15329.
– On an average, profit achieved in Meta Cost on test
set is \$1751 lower than the profit achieved in case of
direct cost-sensitive decision making.
Statistical Significance of Results
• 4872 donors in fixed test set
• Average donation of \$15.62
• Different Test set drawn randomly from same
probability distribution would expect a standard
deviation of sqrt(4872)
• Fluctuation will cause a change of about \$1090.
sqrt(4872) * 15.62 = \$1090.
• Profit Difference between two methods less than
\$1090 is not significant.
Conclusions
• Cost sensitive learning is better than Meta cost.
• Provides solution to fundamental problem of cost
being example dependent.
• Identify and solves the problem of Sample
Selection Bias for KDD’98 dataset
Questions?

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