ECLT5810 E-Commerce Data Mining Techniques SAS Enterprise Miner Neural Network

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1 Enterprise Miner Neural Netwrk 1 ECLT5810 E-Cmmerce Data Mining Techniques SAS Enterprise Miner Neural Netwrk A Neural Netwrk is a set f cnnected input/utput units where each cnnectin has a weight assciated with it. During the learning phase, the netwrk learns by adjusting the weights s as t be able t predict the crrect class label f the input samples. A. Neural Netwrk Nde By default, the Neural Netwrk nde autmatically cnstructs a multilayer feed-frward netwrk that has ne hidden layer cnsisting f three neurns. In general, each input is fully cnnected t the first hidden layer, each hidden layer is fully cnnected t the next hidden layer, and the last hidden layer is fully cnnected t the utput. The Neural Netwrk nde supprts many variatins f this general frm. Basically, the neural netwrk nde has the fllwing settings: Data Tab Variables Tab General Tab Basic Tab Advanced Tab Output Tab I. Data Tab The Data tab cnsists f tw subtabs: General -- sets the predecessr data sets fr training, validatin, testing, and scring. Different data sets are used as fllws.

2 Enterprise Miner Neural Netwrk 2 1. The Training data set is used t train (estimate) the netwrk weights. 2. The Validatin data set is used t chse netwrk prducing the lwest unbiased average errr. 3. The Test data set is used t btain an unbiased estimate f generalizatin errr after yu have chsen a netwrk based n validatin errr. Optins -- specifies whether r nt t use a sample f the DMDB fr preliminary training, and specifies the maximum number f rws in the training data set t permit interactive training. II. Variables Tab Similar t ther ndes, the variables tab can be used t view the data set variables, their mdel rles, measurements, types, frmats, and labels, and als set their status, edit the target prfile. III. General Tab In the General tab, we can specify the mdel selectin criteria set the user interface specify whether r nt t accumulate training histry specify hw yu want t mnitr the training prcess. I. Mdel Selectin Criteria Average Errr -- chses the mdel that has the smallest average errr fr the validatin data set. Misclassificatin Rate -- chses the mdel that has the smallest misclassificatin rate fr the validatin data set. Prfit/Lss (Default) -- If yu defined tw r mre decisins in the prfit r lss matrix, then the nde chses the mdel that maximizes the prfit r minimizes the lss fr the cases in the validatin data set. If a validatin data set is nt available, then the nde uses the training data set. If the decisin matrix cntains less than tw decisins, then the

3 Enterprise Miner Neural Netwrk 3 II. III. IV. prfit r lss infrmatin is nt cnsidered in the mdel selectin prcess. When there are less than tw decisins, the average errr criterin is actually used fr mdel selectin. T use the prfit/lss criterin, yu must define a prfit r lss matrix in the target prfile fr the target. The prfit r lss values are adjusted fr the prir prbabilities that yu specify in the prir vectr f the target prfile. T learn mre abut defining a target prfile, read the Target Prfiler chapter. Advanced user interface Accumulating Training Histry Mnitring the Training Prcess IV. Basic Tab Cnfiguratins f the basic tab include: Netwrk architecture -- the default is multilayer perceptrn - n direct cnnectins, and the number f hidden neurns is data dependent. Preliminary runs -- the default is nne. Training technique -- the default depends n the number f weights applied during executin. Runtime limit -- the default is 2 hurs. Set Netwrk architecture Hidden neurns -- If yu select the number f hidden neurns based n the nise in the data (any f the first fur items), the number f neurns is determined at run time and based n the ttal number f input levels, ttal number f target levels, and the number f training data rws in additin t the nise level. High nise data Mderate nise data Lw nise data Niseless data Set number Direct cnnectins By default, the netwrk des nt include direct cnnectins. In this case, each input unit is cnnected t each hidden unit and each hidden unit is cnnected t

4 Enterprise Miner Neural Netwrk 4 each utput unit. If yu set the Direct cnnectins value t Yes, then each input unit is als cnnected t each utput unit. Direct cnnectins define linear layers, whereas hidden neurns define nnlinear layers. Netwrk architecture Generalized Linear Mdel Multilayer Perceptrn -- default. Ordinary RBF-Eq. Widths -- rdinary radial basis functin with equal widths. Ordinary RBF-Uneq. Widths -- rdinary radial basis functin with unequal widths. Nrm. RBF-Eq. Heights -- nrmalized radial basis functin with equal heights. Nrm. RBF-Eq. Vlumes -- nrmalized radial basis functin with equal vlumes. Nrm. RBF-Eq. Widths -- nrmalized radial basis functin with equal widths. Nrm. RBF-Eq. Widths and Heights -- nrmalized radial basis functin with equal widths and heights. Nrm. RBF-Uneq. Widths and Heights -- nrmalized radial basis functin with unequal widths and heights. All f the nrmalized radial basis functins are grayed ut if the number f hidden neurns is set t 1. Depending n the cmplexity f the data and the underlying netwrk architecture, neural netwrks can take a lng time t cnverge. V. Advanced Tab In the Advanced tab, yu specify advanced netwrk settings in the fllwing subtabs: Netwrk Subtab Initializatin Subtab Optimizatin Subtab Train Subtab Prelim Subtab T activate the Advanced tab, yu must first select the Advanced user interface check bx in the General tab. When yu select the Advanced tab, the Netwrk subtab is displayed. 1. Netwrk Subtab create the netwrk, using the same set f templates that are prvided in the basic user interface f the Basic tab diagram the netwrk, making cnnectins as required add hidden layers and cntrl the number f units view and set cnnectin prperties, specifying distributin, scale parameter, lcatin parameter, and randm seed view and set nde prperties and grup variables int ndes as needed.

5 Enterprise Miner Neural Netwrk 5 Diagramming the Netwrk Left: inputs Middle: the hidden layers Right: utputs (crrespnd t the targets, e.g. GOOD_BAD) By default, the input ndes are gruped by type, and nly the input-nde types are shwn in the diagram. The default grupings f the ndes are explained here: All interval input variables are gruped int ne interval-type nde: Interval. All nminal input variables are gruped int ne nminal-type input nde: Nminal. All rdinal input variables are gruped int ne rdinal-type input nde: Ordinal. All interval target variables are gruped int ne interval-type target nde: T_intrvl. Each nminal target variable is given its wn separate nde. Each rdinal target variable is given its wn separate nde. Gruped variables can be placed int separate ndes, and separate ndes f the same type can be cmbined t frm a gruped nde. Mdifying the netwrk diagram: Select ndes and cnnectins by left-clicking Right-click t display a menu f actins: Prperties, Add hidden layer, etc. After adding the hidden layer, yu may want t examine r change its prperties. Fr example, yu may want t change the number f neurns in the hidden layer by viewing and setting cnnectin prperties: select a cnnectin arrw in the diagram right-click t display a menu f actins select Prperties viewing and setting nde prperties select a nde in the diagram right-click t display a menu f actins select Prperties Nte: Variables can be mved as desired amng cmpatible ndes. Fr example, interval input variables can be separated r merged with ther interval input variables, but they cannt be merged with nminal input variables. Beware that there are relatinships amng the neural netwrk ptin settings. Sme cmbinatins f ptin settings are incmpatible, and the netwrk cannt be trained. The default settings are cmpatible and are highly recmmended. The default settings are shwn in green, and they have been carefully selected. If yu change a default setting, then the new setting is shwn in black. When yu reset ne r mre ndes, the default settings are restred. Optin settings that are incmpatible with previus selectins are grayed in subsequent menus.

6 Enterprise Miner Neural Netwrk 6 2. Initializatin Subtab Generate a randm seed, by clicking the Generate New Seed buttn. The randm seed effects the starting pint fr training the netwrk. If the starting pint is clse t the final settings, then the training time can be dramatically reduced. Cnversely, if the starting pint is nt clse t the final settings, then training time tends t increase. Yu may want t first accept the default setting, and then in later runs, specify ther randm seeds. Specify Randmize: scale estimates, target weights, and target bias weights. Select Starting values fr training: Nne: default, Current Estimates: uses the mst recent training weights. Selected data set.. 3. Optimizatin Subtab Optimizatin Step Train (default) -- the mdel is trained when selected. Preliminary -- perfrms preliminary ptimizatin. Train and Preliminary -- perfrms preliminary ptimizatin, and then, fr training, uses the weights that yield the best bjective functin value fr the preliminary ptimizatins. Early stpping -- defines a set f training parameters t hasten cnvergence fr cmplex mdels. Evaluate Functin -- cmputes the bjective functin using the current weights. Objective Functins: Default -- the nde chses an bjective functin that is cmpatible with all f the specified errr functins. Deviance -- the difference between the likelihd fr the actual netwrk and the likelihd fr a saturated mdel in which there is ne weight fr every case in the training data set.

7 Enterprise Miner Neural Netwrk 7 Maximum Likelihd -- the negative lg likelihd is minimized. M Estimatin -- primarily used fr rbust estimatin when the target values may cntain utliers. Weight Decay Parameter, which is a numeric parameter: the larger the value, the greater the restrictin n ttal weight grwth. The default is zer. Cnvergence Parameters, NLP - set parameters cmmn t nnlinear ptimizatin prcesses. Adjustments - adjust the abslute cnvergence parameter fr each f the 4. Train Subtab Training is finding the ptimal weights fr the neural netwrk. The gal f training is t find the weights that yield a glbal minimum fr the bjective functin. Training Technique. The mst ppular cnventinal ptimizatin algrithms include Levenberg-Marquardt, Quasi-Newtn, and Cnjugate Gradient. The Levenberg-Marquardt technique is recmmended fr smth least squares bjective functins and netwrk architectures with a small number f weights (up t 100). The Quasi-Newtn technique is recmmended fr netwrk architectures with a mderate number f weights (up t 500). The Cnjugate Gradient technique is recmmended fr netwrk architectures with a large number f weights (mre than 500). Back-prpagatin-based training technique: Standard backprp is the mst ppular back prpagatin methd, but it is slw, unreliable, and requires the user t tune the learning rate manually, which can be a tedius prcess. Quickprp and RPROP tend t take mre iteratins than cnjugate gradient methds, but each iteratin is very fast. Other cnventinal training techniques include Duble Dgleg, Trust Regin, and Newtn-Raphsn with Line Search r Ridging. Minimum Iteratins. The default is missing, which indicates that the value will be set at run time based n the size f the training data and the netwrk architecture. Maximum Iteratins. The default is missing, which indicates that the value will be set at run time based n the size f the training data and the netwrk architecture. Maximum CPU Time. The default is 168 hurs. 5. Prelim Subtab Preliminary ptimizatin is useful fr finding gd starting values fr the netwrk weights. Perfrming preliminary ptimizatin may help yu avid finding weights that yield a lcal minimum fr the bjective functin. When yu perfrm preliminary ptimizatin, it is advisable t d at least 10 preliminary runs. VI. Output Tab 1. Select data sets r scring (r using the scre nde instead). 2. Select data sets fr brwsing 3. View prperties f utput datasets. B. Running the Neural Netwrk Nde The Neural Netwrk nde can be run in a nn-interactive mde and in an interactive mde. When the nde is running, the SAS Prcess Mnitr displays graphically the errr functin after each ptimizatin iteratin (prvided that yu did nt disable the mnitr in the Basic tab). Yu can use the mnitr t interrupt training, cntinue training, r stp training altgether. T stp

8 Enterprise Miner Neural Netwrk 8 prcessing the current iteratin, select Stp Current. T stp prcessing all iteratins, select Stp All. T clse the mnitr, select Clse. C. Neural Netwrk Results Brwser After yu run the nde in nn-interactive r interactive mde, yu can view the results in the Results Brwser. The Results Brwser cntains the fllwing tabs: Mdel Tab Tables Tab Weights Tab Plt Tab Cde Tab Lg Tab Output Tab Ntes Tab 1. Mdel Tab The Mdel tab cntains the fllwing subtabs: General -- prvides administrative infrmatin abut the mdel and enables yu t view target prfile infrmatin. Netwrk -- summarizes the netwrk settings. 2. Tables Tab The Tables tab displays utput data sets. Use the drp-dwn arrw t select a data set. This example shws the fllwing data sets. Current estimates -- the bjective functin values and the weight (parameter) values fr the current netwrk. Current statistics -- the gdness-f-fit statistics fr the current netwrk. New Plt -- the summary statistics fr the iteratins. 3. Weights Tab The Table subtab displays the current netwrk weights.

9 Enterprise Miner Neural Netwrk 9 The Graph subtab displays a plt f the current netwrk weights. The tlbx enables yu t interact with the grid plt. View Inf -- click n this tl, then click n a bar (weight) t display infrmatin abut that weight. Mve -- click n this tl, and then click and drag the histgram t the desired lcatin. Scrll -- click n this tl and then slide yur cursr t scrll thrugh the grid plt f weights. Reset -- click n this tl t reset the histgram t its initial display settings. 4. Plt Tab By default, the Plt tab displays a plt f the average errr functin values as the bjective functin is minimized. Fr each iteratin the parameter values are updated, and hence the value f the errr functin changes. In general, the errr functin value fr the training data set decreases acrss iteratins. The gal is t find parameter values that minimize the errr functin.

10 Enterprise Miner Neural Netwrk 10 Nte: T display a plt f the prfit-r-lss values fr every iteratin, right-click n the plt and select either the Prfit r Lss pp-up menu item. When yu click n a pltted value, the vertical white line mves t that iteratin, and it becmes the current netwrk. When yu right-click in this tab, a pp-up menu appears: New plt -- prvides a new plt f the selected variables. Histry plt -- prvides a plt histry f the selected variables. Errr -- plts the errr functin values fr the iteratins. Prfit -- plts the prfit values fr the iteratins. Lss -- plts the lss values fr the iteratins. Statistics -- enables yu t select statistics t be pltted. Weights -- enables yu t select weights t be pltted. A plt f the weights is useful fr studying the stability f the weights. Enable ppup data inf -- when enabled and yu click n the plt, a pp-up display shws the run number, the ptimizatin step, the iteratin number, and the bjective functin values fr the data sets. Select Disenable ppup data inf t disable the pp-up display. Set netwrk at displays a set f chices: Set netwrk at selected iteratin and Set netwrk at minimum errr functin (sets the netwrk at the iteratin that minimizes the validatin data set). 5. Cde Tab The Cde tab displays the autmatically generated SAS sftware cde. 6. Lg Tab The Lg tab displays the lg f the ptimizatin prcess. 7. Output Tab The Output tab displays the printed utput that was generated frm the submitted cde: Preliminary iteratin histry Optimizatin start parameter estimates (netwrk weights) Levenberg-Marquart ptimizatin criteria Iteratin statistics fr the Levenberg-Marquart Optimizatin Optimizatin results (final) parameter estimates Objective functin value at the last iteratin (nt shwn n the display, but listed after the parameter estimates).

11 Enterprise Miner Neural Netwrk 11 D. Output Data Sets frm the Neural Netwrk Nde In the Data subtab f the Output tab f a Neural Netwrk nde, yu can select the variables that are included in the scred training, validatin, test, r scre data set: S_ -- standardized variables H<number> -- hidden units P_ -- psterir prbabilities fr categrical targets r predicted values fr interval targets I_ -- nrmalized categry that the case is classified int U_ -- unnrmalized categry that the case is classified int D_ -- label f the decisin that is chsen by the mdel E_ -- errr functin R_ -- residuals EP_ r EL_ -- expected prfit r lss fr the decisin that is chsen by the mdel IC_ -- investment cst. E. Assessing the Mdels Add an Assessment nde Cnnect each mdel nde t the Assessment nde. Open the Assessment nde. Select all three mdels by dragging acrss each mdel rw. T create a lift chart, use Tls, and select Lift Chart. Select the mdel fr subsequent scring. Select the Output tab. Click n Neural Netwrk entry in the Highlight Mdel fr Output list bx. References: Han, J., and Kamber, M. (2001). Data Mining: Cncepts and Techniques. Mrgan Kaufman. SAS Enterprise Miner.

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