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Chapter 16. Scoring data with predictive models
Theprocessof applying apredictive modelto aset of dataisreferred to asscoring the data.IBMSPSS
Statisticshasproceduresfor building predictivemodelssuch asregression,clustering, tree,andneural
network models.Onceamodelhasbeenbuilt, the modelspecificationscan be savedin afile that
containsallof the informationnecessary toreconstructthe model.You canthenusethat model file to
generate predictive scoresinotherdatasets. Note:Some proceduresproduce amodelXMLfile, andsome
proceduresproduce acompressed file archive (.zip file).
Example. The direct marketing divisionofa company usesresultsfromatest mailing to assign
propensity scoresto the rest of their contact database, using variousdemographic characteristicsto
identifycontactsmost likely torespond and makeapurchase.
Scoring istreatedasatransformation of the data. The model isexpressedinternally asaset of numeric
transformationsto be applied to agivenset of fields(variables)--thepredictorsspecified inthe model--in
ordertoobtainapredicted result. Inthissense, the processof scoring datawithagivenmodelis
inherently the sameasapplyingany function, such asasquare root function,toaset of data.
Thescoring processconsistsof two basicsteps:
1. Buildthe modeland save themodelfile.You buildthe modelusing adataset forwhichthe outcome
of interest (oftenreferred to asthe target) isknown. For example, ifyou want to builda modelthat
willpredict who is likely to respondtoadirect mailcampaign, you need to start withadataset that
alreadycontainsinformation onwho responded and who did notrespond.For example,thismight be
the resultsof atestmailing to asmallgroupof customersor informationonresponsestoasimilar
campaigninthe past.
Note:For some modeltypesthere isno targetoutcome of interest.Clustering models, for example, do
not haveatarget,andsome nearestneighbor modelsdo not havea target.
2. Apply thatmodeltoadifferent dataset (for whichthe outcome of interest isnot known) to obtain
predictedoutcomes.
Scoring Wizard
Youcanusethe Scoring Wizardtoapply amodel created withone dataset to another dataset and
generate scores,suchasthe predictedvalue and/or predictedprobability of an outcome of interest.
Toscore adataset withapredictive model
1. Openthe datasetthat you want toscore.
2. Openthe Scoring Wizard. Fromthe menuschoose:
Utilities >Scoring Wizard.
3. Select amodelXML file orcompressed file archive(.zipfle). Use the Browsebuttonto navigate to a
different locationto select amodelfile.
4. Match fieldsinthe active dataset to fieldsusedinthe model.Seethetopic“Matchingmodelfieldsto
datasetfields”onpage 184for more information.
5. Select the scoring functionsyouwanttouse. See the topic“Selecting scoringfunctions”onpage 185
formore information.
Selecta ScoringModel. The model file canbe anXMLfile or acompressedfilearchive (.zip file) that
containsmodelPMML.The list only displaysfileswith a.zip or .xmlextension; the file extensionsare
not displayed inthe list.You canuse any modelfile created by IBMSPSSStatistics.You canalsouse
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some modelfilescreated by other applications, suchas IBMSPSS Modeler, butsome model filescreated
by otherapplicationscannot be readby IBMSPSS Statistics, including any modelsthat have multiple
targetfields(variables).
Model Details. Thisareadisplaysbasicinformation about the selected model, suchas modeltype, target
(if any), and predictorsused to buildthemodel.Since the modelfilehasto be read to obtainthis
information, there may beadelaybefore thisinformationisdisplayed for the selectedmodel.If the XML
file or .zipfile isnot recognized asamodelthat IBMSPSSStatisticscanread, amessage indicating that
the file cannot be read isdisplayed.
Matching model fields to dataset fields
Inorder to score theactive dataset,thedataset must containfields(variables) that correspond to all the
predictorsinthe model. Ifthemodelalso containssplit fields, thenthedataset must also containfields
thatcorrespondtoallthe split fieldsinthe model.
v By default, any fieldsinthe active datasetthat havethesame name andtype asfieldsinthe modelare
automatically matched.
v Use the drop-downlist to matchdataset fieldsto model fields. Thedata type foreach field must be the
same inboththe modeland the datasetinorder to matchfields.
v You cannot continuewiththe wizard or score the active dataset unlessallpredictors(and split fieldsif
present) in themodelare matchedwith fieldsinthe activedataset.
Dataset Fields. Thedrop-downlist containsthe namesof allthe fieldsinthe active dataset. Fieldsthat
do notmatchthe datatype of the corresponding model field cannot be selected.
Model Fields. The fieldsused inthe model.
Role. Thedisplayed role can be one of the following:
v Predictor. Thefieldisused asapredictorinthemodel.That is, values of the predictorsare used to
"predict"valuesof the target outcome of interest.
v Split. The valuesof the split fieldsare used to define subgroups, whichareeach scoredseparately.
Thereisaseparate subgroupfor each unique combinationof split fieldvalues. (Note: splitsare only
availablefor some models.)
v Record ID. Record (case)identifier.
Measure. Measurementlevel for the fieldasdefinedinthe model.For modelsinwhichmeasurement
levelcanaffectthe scores, the measurement level asdefined inthe model isused,not the measurement
levelasdefinedin theactivedataset.For more informationon measurement level, see “Variable
measurementlevel”onpage 51.
Type.Datatypeasdefined in themodel. Thedata type inthe active dataset must match thedatatype in
the model. Datatype canbe oneof thefollowing:
v String. Fieldswithadatatype of stringin theactivedataset matchthe datatypeof string inthe
model.
v Numeric. Numericfieldswithdisplay formatsotherthandate or time formatsin theactive dataset
matchthe numericdatatype inthe model. ThisincludesF (numeric), Dollar, Dot, Comma,E (scientific
notation), and customcurrency formats. FieldswithWkday (day of week) and Month(month of year)
formatsarealsoconsiderednumeric, not dates. For somemodeltypes, date and time fieldsinthe
active dataset arealso considered amatchforthe numeric datatype inthe model.
v Date. Numericfieldswithdisplay formatsthat include the date but not the time inthe active dataset
matchthe date typeinthe model.ThisincludesDate(dd-mm-yyyy), Adate (mm/dd/yyyy), Edate
(dd.mm.yyyy), Sdate (yyyy/mm/dd), and Jdate (dddyyyy).
v Time. Numeric fieldswithdisplay formatsthat include thetime butnot the date inthe active dataset
matchthe time datatypeinthemodel.ThisincludesTime (hh:mm:ss) andDtime(ddhh:mm:ss)
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v Timestamp. Numeric fieldswithadisplay format that includesboth the dateandthe time inthe active
dataset match thetimestamp datatype inthe model.Thiscorrespondsto the Datetime format
(dd-mm-yyyy hh:mm:ss) inthe active dataset.
Note:Inadditionto field name and type,youshould make sure that the actualdatavaluesinthe dataset
being scored are recorded inthe same fashionasthe datavaluesinthe dataset used to buildthemodel.
For example, ifthemodelwasbuilt withanIncomefield thathasincome divided intofour categories, and
IncomeCategoryinthe active dataset hasincome dividedinto sixcategoriesorfour different categories,
those fieldsdon't really matcheach other and the resultingscoreswillnot be reliable.
Missing Values
Thisgroup of optionscontrolsthe treatment of missing values,encounteredduring the scoringprocess,
for the predictor variablesdefined inthe model.Amissing value in thecontext of scoringrefersto one of
the following:
v Apredictor containsno value. Fornumeric fields(variables),thismeansthe system-missing value. For
string fields, thismeans anull string.
v The value hasbeendefined asuser-missing, inthe model, forthe given predictor.Valuesdefinedas
user-missing inthe active dataset, but not inthe model, arenot treated as missing valuesinthe scoring
process.
v The predictoriscategoricalandthe value isnot one of the categoriesdefined in themodel.
UseValueSubstitution. Attempt to use valuesubstitutionwhenscoring caseswithmissing values.The
method fordetermininga valuetosubstitute foramissing valuedependsonthe type of predictive
model.
v Linear Regressionand Discriminant models. Forindependentvariablesinlinearregressionand
discriminantmodels, if meanvalue substitution for missing valueswasspecified whenbuilding and
savingthemodel,thenthismeanvalue isusedinplace of the missing value inthe scoring
computation, and scoring proceeds. If the meanvalue isnot available,thenthe system-missing value is
returned.
v Decision Treemodels. Forthe CHAIDandExhaustive CHAID models, the biggestchildnode is
selected for amissingsplit variable. The biggestchildnode isthe one withthe largest population
amongthe childnodesusing learning sample cases. ForC&RT and QUEST models,surrogate split
variables(if any) are used first. (Surrogate splitsaresplitsthat attempt to matchthe originalsplit as
closely aspossible using alternate predictors.) If no surrogatesplitsare specified or allsurrogate split
variablesare missing, the biggest child node isused.
v Logistic Regressionmodels.For covariatesinlogisticregressionmodels, if amean valueof the
predictor wasincluded aspartof thesavedmodel,thenthismeanvalue isusedinplace of the
missing valueinthescoring computation, and scoring proceeds. If the predictoriscategorical(for
example, afactor inalogisticregression model), or if the meanvalue is notavailable, then the
system-missing value isreturned.
UseSystem-Missing. Returnthe system-missing value whenscoring acase withamissing value.
Selecting scoring functions
Thescoring functionsare the typesof "scores"availablefor the selectedmodel.For example, predicted
value of the target, probability of the predicted value, or probability of aselected target value.
Scoring function.The scoringfunctionsavailable are dependent on the model. One or moreof the
following willbeavailable in the list:
v Predictedvalue.The predicted valueof thetarget outcome ofinterest. Thisisavailable forallmodels,
exceptthose that do nothave atarget.
Chapter16.Scoringdatawithpredictivemodels
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v Probabilityofpredictedvalue.The probability of the predictedvalue being the correct value,
expressed asaproportion. Thisisavailable for mostmodelswithacategoricaltarget.
v Probabilityofselected value. The probability of the selectedvalue being the correct value, expressed
asaproportion. Select avalue fromthe drop-downlistintheValue column.The availablevaluesare
defined by the model. Thisisavailable formost modelswith acategorical target.
v Confidence. Aprobability measure associated withthe predicted value of acategorical target. For
Binary Logistic Regression, Multinomial Logistic Regression, and NaiveBayesmodels, the resultis
identicaltotheprobability of the predictedvalue.For Tree and Ruleset models, the confidence canbe
interpretedasanadjusted probability ofthe predicted category and isalwayslessthanthe probability
of the predicted value. Forthese models, the confidence value ismore reliable thanthe probability of
the predicted value.
v Nodenumber. The predictedterminal nodenumberfor Tree models.
v Standarderror.The standard errorofthe predicted value. Available for Linear Regressionmodels,
GeneralLinear models,andGeneralizedLinear modelswith ascaletarget.Thisisavailableonly if the
covariance matrixissaved inthe modelfile.
v Cumulative Hazard.The estimated cumulative hazard function. The value indicates theprobability of
observing the event at or beforethe specifiedtime,given the valuesof the predictors.
v Nearest neighbor. TheID of the nearest neighbor. The ID isthe value of the case labelsvariable, if
supplied, and otherwise the case number.Appliesonly to nearest neighbor models.
v Kth nearest neighbor. The ID of the kthnearest neighbor. Enter aninteger for the value of kinthe
Value column.The ID isthe value of the case labelsvariable,if supplied, and otherwise the case
number.Applies only tonearest neighbormodels.
v Distanceto nearest neighbor. The distancetothe nearest neighbor. Depending onthemodel,either
EuclideanorCity Block distance willbe used. Appliesonly to nearestneighbor models.
v Distanceto kthnearest neigbor. The distancetothe kthnearest neighbor. Enteraninteger forthe
value of k in theValue column. Depending onthe model,either Euclideanor City Block distancewill
beused.Appliesonly to nearest neighbor models.
FieldName.Eachselected scoring functionsavesanew field (variable) inthe active dataset.You canuse
the default names orenter new names. If fieldswiththose namesalready exist in theactive dataset,they
willbe replaced. For informationon field naming rules, see “Variablenames”onpage 50.
Value.Seethedescriptionsof the scoringfunctionsfor descriptions of functionsthat use aValue setting.
Scoring the active dataset
Onthefinalstepof the wizard,youcanscore the active datasetorpaste the generated commandsyntax
to asyntaxwindow.You can thenmodifyand/or save the generated commandsyntax.
Merging model and transformation XML files
Some predictivemodelsarebuilt withdatathathave beenmodified or transformed invariousways. In
ordertoapply thosemodelsto otherdatasets in ameaningfulway,the same transformationsmust also
be performedon the dataset being scored,orthe transformationsmust alsobe reflected inthe model file.
Includingtransformationsinthe modelfileisatwo-stepprocess:
1. Save the transformationsin atransformation XMLfile. This can only be done using TMSBEGINand
TMS END incommand syntax.
2. Combine the modelfile (XMLfile or .zipfile) andthe transformationXMLfileinanew, merged
model XMLfile.
To combine amodelfile and atransformationXMLfile inanew, merged modelfile:
3. Fromthe menuschoose:
Utilities >MergeModel XML
4. Select the modelXMLfile.
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5. Select the transformation XMLfile.
6. Enter apathandname for the newmerged modelXMLfile,oruse Browsetoselect the locationand
name.
Note:You cannot merge model.zipfilesformodelsthat containsplits(separate modelinformation for
eachsplit group) or ensemblemodelswithtransformationXML files.
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Chapter 17. Utilities
Utilities
Thischapterdescribesthe functions found onthe Utilitiesmenuandthe ability to reorder target variable
lists.
v For informationon theScoring Wizard, see Chapter16, “Scoringdatawithpredictive models,”onpage
183.
v For informationon merging modelandtransformationXMLfiles,see “Merging model and
transformationXMLfiles”onpage 186.
Variable information
TheVariablesdialog box displaysvariable definitioninformationfor the currentlyselected variable,
including:
v Variable label
v Data format
v User-missingvalues
v Value labels
v Measurementlevel
Visible. The Visible columninthe variable list indicatesif the variable iscurrently visible inthe Data
Editorandindialog box variable lists.
Go To. Goesto the selectedvariableintheDataEditor window.
Paste.Pastestheselected variablesinto the designated syntaxwindow at the cursor location.
Tomodify variable definitions, use the Variable viewinthe DataEditor.
ToObtainVariable Information
1. Fromthe menus choose:
Utilities >Variables...
2. Select the variable forwhichyouwant to display variable definition information.
Data file comments
Youcaninclude descriptive commentswith adatafile. For IBMSPSS Statisticsdatafiles, these comments
are saved withthedata file.
Toadd, modify, delete,ordisplaydata file comments
1. Fromthe menus choose:
Utilities >Data FileComments...
2. Todisplaythecomments in theViewer, select Displaycommentsin output.
Comments canbeany length but are limited to 80 bytes(typically 80charactersinsingle-bytelanguages)
per line;lineswill automatically wrapat80characters. Commentsare displayedinthe same font astext
output toaccurately reflect howthey will appear whendisplayed inthe Viewer.
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189
Adate stamp (the current date inparentheses) isautomatically appendedto theendof the list of
commentswhenever youaddormodify comments. Thismay lead to some ambiguity concerning the
datesassociatedwithcommentsif you modify anexistingcomment or insert anewcomment between
existingcomments.
Variable sets
Youcanrestrict the variablesthat are displayed inthe DataEditor and indialogboxvariable listsby
defining and using variable sets. Thisisparticularly useful for datafileswithalarge number of variables.
Smallvariablesetsmakeit easier to findandselect the variablesfor youranalysis.
Defining variable sets
DefineVariable Setscreatessubsetsof variablesto display inthe DataEditor and in dialog boxvariable
lists. Defined variable setsaresavedwithIBMSPSS Statisticsdatafiles.
SetName.Set namescanbe upto 64bytes. Any characters, including blanks,canbeused.
VariablesinSet. Any combinationof numeric and string variablescanbe includedinaset. The order of
variablesintheset hasno effect on thedisplayorder of the variablesin theDataEditoror indialog box
variable lists.Avariablecanbelong to multiple sets.
Todefine variable sets
1. Fromthe menuschoose:
Utilities >DefineVariableSets...
2. Select the variablesthat youwant to include inthe set.
3. Enter aname for theset (upto 64bytes).
4. Click Add Set.
Using variable sets to show and hide variables
Use VariableSetsrestrictsthe variables displayed inthe DataEditor and indialogboxvariable liststo the
variablesintheselected (checked) sets.
v The set of variablesdisplayed inthe DataEditor and in dialog boxvariable listsisthe unionof all
selected sets.
v Avariable canbe included in multiple selected sets.
v The orderof variablesinthe selectedsetsand the orderof selected setshave noeffectonthe display
order of variablesintheDataEditor or indialog box variable lists.
v Althoughthe defined variable setsare savedwithIBMSPSS Statisticsdatafiles, the list of currently
selected setsisreset tothe default, built-in setseachtime youopenthe datafile.
The list of available variable setsincludesany variable setsdefined for the active dataset, plustwo
built-insets:
v ALLVARIABLES. Thisset containsallvariablesinthe datafile, including new variables created
during asession.
v NEWVARIABLES.Thisset contains only new variablescreated during thesession.
Note: Evenif you savethedata file after creating newvariables,the new variablesare stillincludedin
the NEWVARIABLES set untilyouclose and reopenthe datafile.
At least one variable set must be selected. If ALLVARIABLESisselected, anyother selectedsetswillnot
have any visible effect, since thisset containsallvariables.
1. Fromthe menuschoose:
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IBMSPSSStatistics23CoreSystemUser'sGuide
Utilities >UseVariableSets...
2. Select the definedvariablesetsthatcontainthe variablesthatyouwant to appear inthe DataEditor
and indialogboxvariablelists.
Todisplayallvariables
1. Fromthe menus choose:
Utilities >ShowAll Variables
Reordering target variable lists
Variablesappear ondialog box target listsinthe orderinwhichtheyare selectedfromthe source list. If
you want to changetheorder of variables onatarget list—but you don't want to deselectallof the
variablesandreselect theminthe new order—youcanmovevariablesupanddownon thetarget list
using the Ctrlkey (Macintosh: Command key) withthe up and downarrow keys.You canmove multiple
variablessimultaneously if they are contiguous(grouped together). Youcannot move noncontiguous
groups of variables.
Extension bundles
Extensionbundlespackage customcomponents, suchascustomdialogsand extensioncommands, sothat
they canbe easily installed.For example,IBMSPSSStatistics- Essentialsfor Python,whichisinstalled by
default with IBMSPSS Statistics, includesaset of Pythonextensioncommandsthat are packaged in
extensionbundlesand installed withSPSS Statistics. AndIBMSPSSStatistics- Essentials for R(available
from the SPSSCommunity website) includesaset of Rextensioncommandsthat are packaged in
extensionbundles.Many more extension bundles,hostedonthe SPSSCommunity website, areavailable
from the DownloadExtensionBundlesdialog, whichisaccessed from Utilities >ExtensionBundles>
Download and InstallExtension Bundles.
Thecomponentsforanextensionbundle might require the Integration Plug-infor Python, or the
IntegrationPlug-infor R,orboth. Formore information, see the topic "HowtoGet Integration Plug-Ins",
under Core System>Frequently Asked Questionsinthe Helpsystem.
Youcanalso create yourownextensionbundles.For example, if youhave created acustomdialog foran
extensioncommand and want toshare thiswith other users, thenyoucancreate anextensionbundle
thatcontainsthe customdialog package (.spd) file, and the filesassociatedwiththe extensioncommand
(the XMLfile that specifiesthe syntax of the extensioncommand and the implementationcode file(s)
writteninPython, Ror Java).The extension bundle isthenwhatyousharewithotherusers.
Creating and editing extension bundles
How to create an extensionbundle
1. Fromthe menus choose:
Utilities >Extension Bundles> CreateExtension Bundle...
2. Enter valuesfor allfieldsonthe Requiredtab.
3. Enter valuesfor any fieldsonthe Optionaltab that are neededfor your extension bundle.
4. Specify atarget file forthe extensionbundle.
5. Click Saveto save the extensionbundle to the specified location. This closesthe Create Extension
Bundle dialog box.
How to edit an existing extensionbundle
1. Fromthe menus choose:
Utilities >Extension Bundles> Edit Extension Bundles...
2. Openthe extensionbundle.
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3. Modify values for any fieldsonthe Required tab.
4. Modify values for any fieldsonthe Optional tab.
5. Specify atarget file forthe extensionbundle.
6. Click Saveto save the extensionbundle to the specifiedlocation. Thisclosesthe Edit Extension
Bundle dialog box.
Required fields for extension bundles
Name. Aunique name to associate withthe extension bundle. It canconsist of upto threewordsand is
not case sensitive. Charactersare restricted to seven-bitASCII. Tominimize thepossibility of name
conflicts, you may want to use amulti-word name, where the first word isanidentifierfor your
organization, suchas aURL.
Files.Click Addtoadd the filesassociatedwith theextensionbundle.Anextensionbundle must at least
include acustom dialog specification (.spd) file, or anXMLspecification filefor anextensioncommand.If
an XMLspecificationfileisincluded thenthebundle mustinclude at least onePython, Ror Java code
file--specifically,afileoftype .py, pyc, .pyo, .R, .class, or .jar.
v Translation filesfor extensioncommands (implemented inPythonor R) includedinthe extension
bundle are addedfromthe Translation CataloguesFolderfield onthe Optionaltab. See the topic
“Optionalfieldsfor extensionbundles”for more information.
v You canadd areadmefiletotheextensionbundle. Specify the filename asReadMe.txt. Usersswillbe
able to accessthereadme file fromthe dialogthat displaysthedetailsfor the extension bundle. You
caninclude localizedversionsof the readme file, specifiedasReadMe_<language identifier>.txt, asin
ReadMe_fr.txtfor aFrenchversion.
Summary.A short descriptionof the extensionbundle, intendedto be displayed asa single line.
Description. Amore detaileddescriptionof the extensionbundle than that provided forthe Summary
field. For example, youmight list the majorfeaturesavailable withthe extensionbundle. Iftheextension
bundle providesawrapper for afunctionfromanRpackage,thenthat should be mentioned here.
Author.The author of the extension bundle. You may wanttoinclude an email address.
Version. Aversionidentifier of the formx.x.x, where eachcomponent of the identifier must be aninteger,
as in 1.0.0. Zeros are impliedif not provided. Forexample, aversionidentifier of 3.1implies3.1.0. The
versionidentifier is independent of the IBMSPSS Statisticsversion.
MinimumIBM SPSS StatisticsVersion.The minimumversionof IBMSPSS Statisticsrequiredtorunthe
extensionbundle.
Note: You caninclude localized versions of the Summary and Descriptionfieldswiththe extension
bundle. Formore information, see the topic “Optionalfieldsfor extensionbundles.”
Optional fields for extension bundles
ReleaseDate.Anoptionalrelease date.No formatting isprovided.
Links.Anoptionalset of URL’stoassociate with theextensionbundle--for example,theauthor'shome
page. The format of thisfieldisarbitraryso be suretodelimit multiple URL'swithspaces,commas, or
some other reasonable delimiter.
Categories. Anoptionalset of keywordswithwhichto associate theextensionbundle whenauthoring an
extensionbundlefor postingtotheSPSS community (http://www.ibm.com/developerworks/
spssdevcentral). Enterone or more values. For example, you mightenter extension_command,spss and
python.Look at the downloadsonthe SPSScommunityfor typicalkeywordsto use.
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