Types of Data Mining

Transcription

1 Data Mining and The Use of SAS to Deploy Scoring Rules South Central SAS Users Group Conference Neil Fleming, Ph.D., ASQ CQE November 7-9, W Systems Co., Inc Types of Data Mining Supervised Classification (target): Logistic regression (discrete outcome) Multiple regression (continuous outcome) Decision trees (discrete outcome) Regression trees (continuous outcome) Neural Nets (discrete and continuous outcomes) Unsupervised Classification (no target) Cluster analysis (K-Means, hierarchal, etc.) Self-Organized maps (SOMS) 147

2 The Goal: Prediction Versus Explanation What type of action will be taken? Regression: Explanation & Prediction Decision trees: Explanation & Prediction Neural Nets: Prediction Decision Trees Finds variables at different levels to best: Maximize hetergeneity between groups Maximize homogeneity within groups Non-linear (interaction) Merges categories that are the same (no statistically significant difference) Discretizes continuous variables (preserving ordinality) Uses missing data 148

3 Picking a Tool Subsidiary of Forrester Research, Inc. examined four data mining products: 1) SAS Enterprise Miner (EM) 2) SPSS Clementine 3) IBM DB2 Intelligent Miner (IM) 4) Oracle Data Mining (ODM) Decision Tree Deliverables Segments data into terminal nodes Provides profiles for explanation &prediction Creates rules for scoring (prediction) 149

4 Decision Tree Algorithms Goals & Methods CHAID (Chi-Square Automatic Interaction Detection) CART (Classification & Regression Trees) Quest Picking the Best Tree Training, Testing, and Validation Cross-Validation with Hold-out samples Metrics: Gains Tables (ROI) & Classification Error 150

5 SAS: Data Mining Leader SAS was chosen as the leader in functionality for: architecture, algorithms, and data access SPSS was chosen as the leader in usability collaboration between statisticians, data preparers, and business analysts. SAS was chosen as the leader in support, with a slight edge over SPSS IBM was noted for its in data-base modeling & deployment of scoring PRICE of Server Version Initial and Renewal (lowest range) SAS EM:$119K/$39K with Base SAS & SAS/STAT needed SPSS Clementine: $75K IBM DB2 IM: $18,750/$3,750 (probably as add-on)through Data Warehouse Standard Edition which includes many other products Oracle ODM: $20K/CPU with different percentages for perpetual licenses 151

6 My company is not a Fortune 100. Another Solution Dedicated software for decision tree modeling 152

7 Node Node 3 Node Node 4 Node Node 5 Node Node 6 Gain Summary by Node Target variable: Has Amex card Target category: Yes Statistics Nodes Node: n Node: % Gain: n Resp: % Index (%) Total Nodes Node:% Gain(%)

8 Gain Summary - In Deciles Target variable: Has Amex card Target category: Yes Statistics Nodes Percentile Percentile: n Gain: n Gain (%) Resp: % ; ; SQL Rules /* Node 3*/ UPDATE <TABLE> SET nod_001 = 3, pre_001 = 0, prb_001 = WHERE ((PAY_WEEK IS NULL) OR (PAY_WEEK <= 1)) AND ((CLASS IS NULL) OR (CLASS <= 3)); /* Node 4*/ UPDATE <TABLE> SET nod_001 = 4, pre_001 = 0, prb_001 = WHERE ((PAY_WEEK IS NULL) OR (PAY_WEEK <= 1)) AND (NOT(CLASS IS NULL) AND (CLASS > 3)); 154

9 Continued /* Node 5*/ UPDATE <TABLE> SET nod_001 = 5, pre_001 = 1, prb_001 = WHERE (NOT(PAY_WEEK IS NULL) AND (PAY_WEEK > 1)) AND ((AGE IS NULL) OR (AGE <= 2)); /* Node 6*/ UPDATE <TABLE> SET nod_001 = 6, pre_001 = 0, prb_001 = WHERE (NOT(PAY_WEEK IS NULL) AND (PAY_WEEK > 1)) AND (NOT(AGE IS NULL) AND (AGE > 2)); Gains % Chart Based on Deciles 155

10 Misclassification Matrix Actual Category No Yes Total Predicted Category No Yes Total Risk Statistics Risk Estimate = (95+47)/323 SE of Risk Estimate = Sqrt[(.45*(1-.45))/323] SAS Log libname in 'e:/notsug'; NOTE: Libref IN was successfully assigned as follows: Engine: V8 Physical Name: e:\notsug 356 %let dsn=credit; Data Assign; SYMBOLGEN: Macro variable DSN resolves to Credit 359 Set in.&dsn; /*SAS Data set coming in to be segmented*/; 360 nod_001=.; 361 pre_001=.; 362 prb_001=.; NOTE: There were 323 observations read from the data set IN.CREDIT. NOTE: The data set WORK.ASSIGN has 323 observations and 8 variables. NOTE: DATA statement used: real time 0.04 seconds cpu time 0.04 seconds 156

11 Proc SQL; /* Node 3*/ 366 UPDATE Assign 367 SET nod_001 = 3, pre_001 = 0, prb_001 = WHERE ((PAY_WEEK IS NULL) OR (PAY_WEEK <= 1)) AND ((CLASS IS NULL) OR (CLASS <= 3)); NOTE: 86 rows were updated in WORK.ASSIGN /* Node 4*/ 371 UPDATE Assign 372 SET nod_001 = 4, pre_001 = 0, prb_001 = WHERE ((PAY_WEEK IS NULL) OR (PAY_WEEK <= 1)) AND (NOT(CLASS IS NULL) AND (CLASS > 3)); NOTE: 79 rows were updated in WORK.ASSIGN /* Node 5*/ 376 UPDATE Assign 377 SET nod_001 = 5, pre_001 = 1, prb_001 = WHERE (NOT(PAY_WEEK IS NULL) AND (PAY_WEEK > 1)) AND ((AGE IS NULL) OR (AGE <= 2)); NOTE: 108 rows were updated in WORK.ASSIGN /* Node 6*/ 381 UPDATE Assign 382 SET nod_001 = 6, pre_001 = 0, prb_001 = WHERE (NOT(PAY_WEEK IS NULL) AND (PAY_WEEK > 1)) AND (NOT(AGE IS NULL) AND (AGE > 2)); NOTE: 50 rows were updated in WORK.ASSIGN. 384 NOTE: PROCEDURE SQL used: real time cpu time 0.19 seconds 0.19 seconds 157

12 385 Data Assign; 386 Set Assign; 387 If prb_001=. then Prob=0; 388 else If pre_001=0 then Prob=1-prb_001; 389 Else if pre_001=1 then Prob=prb_001; 390 /* This assigns the Probability for Target Outcome 1 */; 391 Run; NOTE: There were 323 observations read from the data set WORK.ASSIGN. NOTE: The data set WORK.ASSIGN has 323 observations and 9 variables. NOTE: DATA statement used: real time 0.05 seconds cpu time 0.05 seconds proc summary data=assign; 394 class nod_001; 395 var Prob;output out=statb mean=mean_prob sum=sum_prob; 396 run; NOTE: There were 323 observations read from the data set WORK.ASSIGN. NOTE: The data set WORK.STATB has 5 observations and 5 variables. Analysis of Credit Card Data 10:32 Monday, April 5, 2004 Segments with Active Cards Dsn=Credit Obs nod_001 _TYPE FREQ_ mean_prob sum_prob

13 Conclusion Use Dedicated Software product that is affordable Combine with SAS SQL for Deploying Scoring Rules Create powerful application for Data Mining Provide explanation that is ACTIONABLE with prediction 159