CART Classification and Regression Trees Experienced User Guide

CART Modeling Strategies Slide 1
CART Modeling Strategies For
Experienced Data Analysts
CART Modeling Strategies For
Experienced Data Analysts
• CART takes a significant step towards
automated data analysis
– One of CART’s predecessors was called
AAutomatic IInteraction DDetector (AIDAID)
• Nevertheless, high quality CART results
require careful planning & expert guidance
• No realistic prospect that CART analyses or
any other sophisticated modeling can be
automated in the near term

All Data analysis, regardless
of methods employed, have
certain prerequisites
All Data analysis, regardless
of methods employed, have
certain prerequisites
• Complete understanding of the data
available
– Correct variable definitions
– Sample sources and relationship to study
population
– Review of conventional summary statistics,
percentiles
– Standard reports that would be generated in the
process of data integrity checks
– Calculations verified: check that totals can be
generated from components
– Consistency checks: related fields do not conflict

Careful data preparationCareful data preparation
• CART is far better suited to dirty data analysis
than conventional statistical modeling or NN tools
– capable of dealing with missing values, outliers
• Nevertheless, considerable benefits to proper
data preparation
– the better the data the better a model can perform
• Includes
– correct identification of missing value codes (998
valid or .)
– uniform data handling when records come from
different entities (branches, regions, behavioral
groups)
– if responder data is processed separately from and
differently than non-responder data, completely
erroneous results will be produced

Some core preparatory stepsSome core preparatory steps
• Identify illegal variables to be excluded from all
models
– ID variables
– post event variables
– variables unlikely to be available in future, or
against which CART model is intended to compete
(eg Bankruptcy scores)
– variables disallowed by regulators (banking,
insurance)
– variables derived in part from dependent variables,
or generated from target variable behavior
– variables too closely connected to target for any
reason

Exploratory Data Analysis with
CART:
Pre-modeling
Exploratory Data Analysis with
CART:
Pre-modeling
• Run a single split tree and report all competitors
– ranks ability of all variables to separate target
variable into homogeneous groups
– command settings
LIMIT DEPTH=1
ERROR EXPLORE
BOPTIONS COMPETITORS=large number
• Run limited depth trees for target using one
predictor at a time (again exploratory--non-tested
trees)
– LIMIT DEPTH=2 (up to 4 nodes) or LIMIT DEPTH=3
(up to 8 nodes) (actual number depends on
redundant node pruning)
– provides optimal binning of variables
– binned versions could be used in parametric models

The CART Non-linear
Correlation Matrix
The CART Non-linear
Correlation Matrix
• Run CART models using every pair of legal
variables
– should be unlimited depth
– could be tested or exploratory
– will detect non-linear dependencies
• Results will be asymmetric
– results can be used to fill out a correlation matrix
• Alternate Procedure
– run simple regressions using all pairs of variables
– use CART to predict residuals
– correlation determined by both linear and CART
components

Example Pearson and CART
correlation Matrices
Example Pearson and CART
correlation Matrices
• From Kerry

CART Affiliation MatricesCART Affiliation Matrices
• Select a group of interesting variables
• Let each variable in turn be the target variable,
all others in group are predictors
• Grow standard trees (not depth limited) with test
procedure to prune
• Each column in matrix is a target variable
• Rows are filled with importance scores (scaled to
0,1)
• Provides a picture of variable interdependencies
• Can highlight surprise relationships between
predictors
– can help in detecting data errors
– when affiliations stringer or weaker than expected

Detection of multivariate
outliers
Detection of multivariate
outliers
• Grow CART tree for every variable as
predicted by a trimmed down variable list
• Predict each variable in turn from all other
variables
• Restrict trees to moderate to large terminal
nodes
– use ATOM or MINCHILD controls
• For regression: measure deviation of each
data point from predicted
• For classification: check if class value of
data point is rare in predicted terminal node
• Use results to investigate unusual
observations

Once data QC is complete
serious CART modeling can
begin
Once data QC is complete
serious CART modeling can
begin
• Need to understand nature of problem:
– what would be the appropriate statistical models to
use for problem at hand
– e.g. is problem a simple binary outcome (respond or
not to a direct mail piece)
– alternatively, does it have an inherent time
dimension (how long will customer remain customer
-- telecommunications churn)
latter problem involves censored data
– is study of a fundamentally time series or panel data
type
– then need to allow for lagged variables, etc.

CART cannot protect you from
using an improper analysis
strategy
CART cannot protect you from
using an improper analysis
strategy
• CART will help you execute your analysis strategy
more quickly and often more accurately
• If the modeling strategy you have selected will
produce biased results CART may just exacerbate
the problem
• A definitive modeling approach is not required,
but a defensible approach is

Example: Targeting model for a
catalog to maximize profit
Example: Targeting model for a
catalog to maximize profit
• Sensible to model in stages
– 1) yes/no response model: use classification tree
– 2) Dollar volume of order for those who do respond
modeled conditional on response=yes
modeled just on subset of responders
regression tree plausible
or classification tree on binned order amounts
– Final model could be an expected profit model
prob(respond)*Expected(Revenue| Respond)
model could be all CART, all logit, or a mixture
such models discussed later

Modeling strategy will also
dictate test strategy
Modeling strategy will also
dictate test strategy
• Suppose we are tracking purchase behavior over
time
• Data organized as one record per purchase
opportunity
• The unit of observation will be a complete case
history
– ideally will want to assign some complete case
histories to training data
– other entire case histories to test data
– important not to allow random assignment between
train and test on a record by record basis
– might want to hold back some records from longer
case histories as an additional source of test data

Initial CART analyses are
strictly exploratory
Initial CART analyses are
strictly exploratory
• Intended to reveal summary and descriptive
information about the data
• Omnibus Model: dependent variable(s) fit to
virtually all legal variables
– Certain obvious exclusions necessary: ID
numbers, clones and transforms of the dependent
variable as discussed above
– Omnibus Model reveals something about the
predictability of the dependent variable
– recall that largest tree has error no more than
twice Bayes rate

Determine Splitting Rule to
Use
Determine Splitting Rule to
Use
• Gini, Twoing, power modified Twoing for
classification
– possibly ordered twoing
• Least squares (LS) or Least Absolute Deviation
(LAD) for regression
• Best splitting rule can be selected very early in
project and typically does not have to be revisited

Assess agreement among
different test methods
Assess agreement among
different test methods
• If data set is small cross validation is required
• In this case rerun trees several times with
different starting random number seeds
– use to assess stability of size and error rate of best
trees
• With large data sets reassign cases between
learn and test several times
– initial check is on error rates and sizes of best trees

Run all as batch of startup
CART trees
Run all as batch of startup
CART trees
• Using three or four splitting rules, and three or
four test sets will get some initial feel for
predictability of target variable
• Useful to develop some text processing scripts to
extract components of the classic CART reports
most interesting
– tree sequence
– misclassification results (which classes are wrong)
– prediction success table
– importance rankings
latter can be aggregated as follows:
add up all importance scores for each variable across
all trees
rescale so that highest score is 100
• LOPTION NOPRINT gives summary tables only
– no tree detail; very helpful when trees tend to be

Derived variables almost
certainly need to be created
Derived variables almost
certainly need to be created
• Almost impossible to develop high performance
models without analyst creation of derived
variables
• Many derived variables are “obvious” to domain
specialists
– to predict purchase amounts look at customer
lifetime totals
– possibly aggregate previous purchases into
category subtotals
– calculate trend; have orders been increasing or
decreasing over time?
• Consider standard statistical summaries of
groups of variables:
– mean, standard deviation, min, max, trend

Use linear combination splits
to search for new derived
variables
Use linear combination splits
to search for new derived
variables
• Linear combinations found by CART can suggest
new derived variables
• Recommend that the delete option be set high
and that the required sample size also be
substantial
• LINEAR N=1000 DELETE=.4
– permits linear combination splits only in nodes with
more than 1,000 cases
– the higher the DELETE parameter the fewer terms in
the combination
• E.g.

Results of first models are
used to generate the first cut
back list of predictors
Results of first models are
used to generate the first cut
back list of predictors
• List is determined through a combination of
judgment and perusal of initial CART runs
• Purpose is error avoidance, exclusion of
nuisance, pernicious and not believable variables
• Variables that seem odd in the context, and thus
probably should not have predictive value also
excluded
– Important not to exclude any variables that prior
knowledge, conventional wisdom would include
– Purpose of this stage is not radical pruning but
elimination of valueless variables

Can be useful to explore trees
for selected predictor variables
or other variables of interest
Can be useful to explore trees
for selected predictor variables
or other variables of interest
• Can think of the CART tree as an extended
non-parametric version of correlation
analysis
• Results simply reveal what variables are in
some way associated in the data
• Could construct a table of variables in the
columns against variables that predict in
the rows

Same procedure could be
used to impute values
for missing data points
Same procedure could be
used to impute values
for missing data points
• Actual procedure is complex and will be
discussed in another context
• Our proposed missing value imputation
procedure is iterative
• Also might start selecting complexity values
that restrain growth of trees to reasonable
sizes
– A large data set might allow trees with many
hundreds of terminal nodes
– Yet optimal models might fall into the 20-100
terminal node size

Next set of models should
explore the impact of
alternative splitting and testing
rules
Next set of models should
explore the impact of
alternative splitting and testing
rules
• Useful to look at GINI, TWOING, and
TWOING POWER=1
• Useful to compare external test data with
cross-validation in smaller data sets
• These runs may suggest which splitting
rules are most promising for further work
• In most problems the default GINI is the
best rule to use
– Definitively better than ENTROPY, often slightly
better than TWOING

Impact of alternative splitting
and testing rules; continued
Impact of alternative splitting
and testing rules; continued
• In some problems, usually problems with
poor predictability, TWOING, POWER=1
works well
– e.g. Relative error in best GINI tree is .8 or
higher
– In these cases, the more balanced splitting
strategy seems to yield better trees

Also want to compare results
from different test procedures
Also want to compare results
from different test procedures
• Compare runs with different subsets of test
data randomly chosen from larger data sets
• e.g., Create two uniform random variables
– %LET TEST20A=urn <0.20
– %LET TEST20B=urn >0.20
– Use TEST20A to pick out test sample in one run
and use TEST20B in another run

We hope results will be very
similar across test sets
We hope results will be very
similar across test sets
• Approximate size of optimal tree
• Approximate relative error
• Importance ranking of variables — which
variables appear near top of list
• Reasonable overlap of primary splitters in
trees

Instability of results across test
data sets is a warning sign
Instability of results across test
data sets is a warning sign
• May need to carefully review interdependencies
of predictor variables
• Results may be due to a set of closely competing
predictors with different information content
• If so, will want to consider whether one or more of
these competitors should be dropped
• In this case, a judgment is made concerning
variables to exclude from the model
• Results may be unstable due to inherent variance
of the tree predictor
• In this case, will ultimately want to consider
aggregation of experts discussed below

Experiments with Linear
Combination Splits
Experiments with Linear
Combination Splits
• Linear combinations are occasionally instructive
• Not useful when many variables are involved
• We recommend restriction to 2-variable linear
combinations
• Helpful if there are strictly positive variables
transformed to logs
– 2-variable linear combination might reveal a form
like
c1*log (X1) - c2*log(X2) ,
which is a ratio of the predictors

Reading CART resultsReading CART results
• Useful to prepare a series of summary reports
after CART runs are done
• One report should just include the TREE
SEQUENCE
– Reveals the size of the optimal tree, relative error
rate
– Can be used to reject certain runs – too large, too
small, too inaccurate
• Another report extracts just the split variables:
– Contains a listing of the node split variables
– Provides an brief outline of how the tree evolved

Reports are used to select
trees that appear to be
promising
Reports are used to select
trees that appear to be
promising
• It is possible that no promising trees are
found in the early rounds of analysis
• Attractive trees need to be printed to
facilitate absorption of the implicit model

Currently we use
allCLEAR to print
Currently we use
allCLEAR to print
• Future CART will include its own pretty print but
will still support allCLEAR
• We request the “splits” level of detail in the
output
– Includes split variable, split value, class assignment
– Table of class distribution in the node might be too
voluminous

Trees need to be read for
the story they tell and
assessed for plausibility
Trees need to be read for
the story they tell and
assessed for plausibility
• Particularly at the higher levels of the tree
(lower levels might disappear with pruning)
• Does the predictive model agree with
intuition and prior expectations?

When troubling patterns
emerge, need to look at the
competitors of a node
When troubling patterns
emerge, need to look at the
competitors of a node
• Reveals what other variable would be used to
split the node if the main splitter were not
available
• If the competitor is more acceptable than the
primary in a node can consider dropping the
primary
• Method will only work if analyst is willing to
exclude the variable from anywhere in the tree
• On the basis of these reports and prints can
determine candidate second round models

Now can move on to tools
for model refinement
Now can move on to tools
for model refinement
• Selection of right-sized trees based on
judgment
• Altering costs of misclassification
• Creation of new variables

Judgmental Pruning of Trees:
A necessary step in
model development
A necessary step in
model development
• When the CART monograph was published in
1984 the authors suggested that the best tree
was the “one-se-rule tree”
• This is the smallest tree within one standard
error of the minimum cost tree
• The reasoning was: all trees within a one
standard error band are statistically
indistinguishable, and small trees are
inherently more comprehensible and preferable

continued
continued
• The current view of the CART originators is that
one should accept the literal minimum cost tree
produced by CART
• This view is based on a further dozen years of
experience which has revealed that the “one-
se-rule” may be too conservative
• Nonetheless, compelling reasons exist to prefer
smaller trees in data-mining investigations

In data-mining exercises
trees can easily grow to
unmanageable depths
In data-mining exercises
trees can easily grow to
unmanageable depths
• With the prodigious volumes of warehoused data, greedy
analysis tools can develop complex models without
restraint
• Paradoxically, the large quantities of data can serve to
mislead
• The problem is similar to that noted by statisticians who
first analyzed large national probability sample
databases: in regression, t-test, and chi-square tests,
almost every estimated coefficient is “significantlysignificantly”
different from zero, and every null is rejected
• In the tree-growing context, elaborate trees of great
depth appear to perform extremely well even on
independent hold-out samples

A way to “discount”
findings based on very
large data sets is needed
A way to “discount”
findings based on very
large data sets is needed
• The solution in the conventional modeling context
has been to adjust the significance level required
before placing too much faith in a finding
• For example, a t-statistic of 2.2 for a regression
coefficient based on 30 degrees of freedom
should be considered more compelling than the
same t-statistic based on 100,000 degrees of
freedom
• In the CART context it would be useful to have
optimal tree size selection criteria that adapted to
the volume of data available

Three tools for adjusting
an analysis to data richness
are available in CART
an analysis to data richness
are available in CART
• The ATOM or minimum node size available
for splitting: as the data set size increases,
ATOM size can also be increased (perhaps
with the log of sample size)
– The thinking is: as data sets increase in size,
require the amount of data needed to support a
split to increase also

an analysis; continued
an analysis; continued
• The minimum child size can also be adjusted.
MINCHILD prevents CART from splitting off nodes too
small to support separate analysis
– For example, we might not want to attempt inferring the
probability of prepay in any node containing less than 100
observations
– MINCHILD and ATOM are closely related but are different
concepts. MINCHILD guarantees that no terminal node will
ever be smaller than its predetermined value. ATOM
determines the minimum size of a node that is eligible to
be split. ATOM must always be at least 2*MINCHILD so
that if the smallest node eligible for splitting is split into
two equal parts, each part will be at least as large as
MINCHILD.
• Trees other than the “optimal” tree can be PICKED from
the tree sequence

The third tool is selection of a
tree from the CART sequence
The third tool is selection of a
tree from the CART sequence
• Analyst intervention in tree selection is both
desirable and unavoidable
• Allows the incorporation of prior knowledge and
domain expertise
• This type of selection is really just pruning: the
analyst decides to prune back further than the CART
algorithms recommend
• Topic is mentioned briefly in the CART monograph
where the authors discuss their decision to eliminate
one or two nodes near the bottom of a medical
diagnosis tree:
– MD’s running the study did not believe that these lower
level splits captured the underlying biology
• This is similar to a statistician deciding to exclude a
borderline significant interaction in a regression

In the data-mining context,
tree selection can be guided by
the relative error plot
In the data-mining context,
tree selection can be guided by
the relative error plot
• Each CART run produces a plot of relative error
against number of nodes and the relative error is
printed on the TREE SEQUENCE report
• In data mining these plots have a characteristic
shape: steep declines in the relative error as tree
initially evolves followed by lengthy flat portions in
which further error reduction is extremely small with
each additional node
• Further, the test data support the hypothesis that
many of these error reductions are “statisticallystatistically
significantsignificant.” In the CART context the claim is that the
more complex larger trees will predict well on fresh
data and thus contain valuable information.

An analyst could defensibly
decide to trade off a large
block
of nodes for a small “increase”
in prediction error
An analyst could defensibly
decide to trade off a large
block
of nodes for a small “increase”
in prediction error• In one of our CART models the “optimaloptimal” tree had
100 terminal nodes and a relative error of 0.333968
+/- 0.00578
• Yet the sub-tree with 63 terminal nodes only has a
relative error of 0.34339, a one-point apparent loss
in accuracy.
• And 29 terminal nodes yield a relative error of .
38564

Final tree selection based on
the relative error plot alone
Final tree selection based on
the relative error plot alone
• In many applications it will be difficult to
make a final tree selection based on the
relative error plot alone
• The plot reveals many opportunities for
selection, but rarely serves to single out a
best tree
• In some problems it is possible to find the
tree that exhausts all substantial
improvements and that separates a steeply
sloping section from a flat plateau

The next step of tree
assessment
The next step of tree
assessment
• Carefully review of a relatively large tree
chosen by CART
• Examination of a large tree node-by-node
will be very instructive
• We are assuming that the early splits of the
tree have already been examined and found
to be convincing and acceptable

Review of a relatively large
tree chosen by CART
Review of a relatively large
tree chosen by CART
• Purpose of this stage of review is to consider the
lower branches:
– Do any of the splits appear fortuitous or not
particularly believable?
– Are the same variables being used repeatedly to
minutely subdivide a predictor?
– Is it worth pursuing additional refinement of the sub-
sample reached at a particular juncture in the tree?
– Is there any concern for whatever reason that the
splits are not reasonable representations of reality?

Additional ConsiderationsAdditional Considerations
• The tree that results when questionable or
low value sections of the CART optimal tree
are dropped should be considered
– Unfortunately, there appears to be no substitute for
the careful and detailed examination of the CART
tree node-by-node
– However, the only contribution of judgment here is
to eliminate nodes that are thought to be the result
of over-fitting

Goodness-Of-Fit Measures
for Classification Trees
in Classic CART
Goodness-Of-Fit Measures
for Classification Trees
in Classic CART
• CART classification trees automatically generate
diagnostic reports
– Relative Error Rate for all trees in pruned sequence
– Misclassification Rate By Class for Learn and Test
data
– Misclassification Table: Actual vs. Predicted Class
• CART class probability trees display only the
relative error sequence
• Although these reports are helpful in sorting out
the most promising trees early on in CART
analyses, they contain far less information than
needed for proper model assessment

Characteristics of the CART
GINI Measure
Characteristics of the CART
GINI Measure
• Measure is zero whenever a node is pure
• Most CART trees are grown and pruned using the
Gini measure of within node diversity
• Gini is largest when distribution of classes in a
node is uniform
• CART trees usually grown with priors EQUAL
– Essential to encourage promising tree evolution
when class distribution is skewed
– Practical impact is to make make CART strive for
roughly equal accuracy in all classes
– Priors DATA and priors MIX rarely work well
• CART Gini measure will then be priors adjusted
i t pi
i
( )= −∑1 2

One new measure of tree
performance — “Rho-squaredRho-squared”
One new measure of tree
performance — “Rho-squaredRho-squared”
• Although the growing process is improved
with equal priors, the practical evaluation of
the tree requires using data priors
– Actual node distributions, not priors adjusted
• We therefore compute unadjusted Gini for
entire tree and compare this with the Gini
of the root
• Provides a measure of the improvement
due to splitting

“Rho-squaredRho-squared”; continued“Rho-squaredRho-squared”; continued
• Formal definition of Rho-squared
Rho-squared = 1 - Gini(tree)/Gini(root)
– If Gini(tree)=Gini(root) we have no improvement
and rho-squared=0
– If Gini(tree)=0, meaning all terminal nodes are
perfectly pure, then rho-squared=1
– Thus, rho-squared measures how the gap from
Gini(root) to a Gini of 0 is closed by the model
• Can be used to compare competing tree
models

Second new measure
compares learn vs. test class
distribution
in terminal nodes
Second new measure
compares learn vs. test class
distribution
in terminal nodes
• Every classification tree generates a distribution
of the dependent variable in each terminal node
• This learn data distribution can be compared with
the distribution observed in other data:
– The test data used to calibrate relative error rates
and select the optimal tree
– A test data set independent of both learn and test
data used in the tree modeling
– Data from other sources that are not necessarily
expected to be similar to the tree under study
• Might also want to compare the test data with
external data

Performance comparisons
can be summarized in
a chi-square statistic
Performance comparisons
can be summarized in
a chi-square statistic
– If there are K classes then each terminal node
contributes a chi-square statistic with K-1 df
– With T terminal nodes the overall statistic for the
tree has T*(K-1) degrees of freedom
– Can decompose the statistic by node or by class
– Useful when the statistic is large to determine
source of large deviations
Are we fitting badly in a specific subtree?
Are the deviations concentrated in one class?

Class Probability TreesClass Probability Trees
• Technically, project Oracle uses class probability
trees for forecasts and simulation
• Class probability trees use the same GINI method
for growing
• Uses GINI for pruning trees as well
• Nevertheless, we used classification trees
throughout and interpreted the results as class
probability trees
• Several reasons for this approach
– Classification trees produce misclassification
reports
– Can be guided by variable cost of misclassification
– Class probability trees sometimes much smaller
than classification trees

Class Probability Trees;
continued
Class Probability Trees;
continued
• Main problem with class probability trees
– Pruning based on equal priors
– Want pruning based on data priors, not yet possible
in CART
• Hence, use of classification tree to allow
judgmental pruning
• Nonetheless, looking at class probability tree
sizes can be used to bound right sized tree
• Would be desirable to modify CAR to allow
different priors in growing and pruning

CART Classification and Regression Trees Experienced User Guide

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CART Classification and Regression Trees Experienced User Guide