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Copyright © 2011, SAS Institute Inc. All rights reserved. “I Don’t Need Enterprise Miner ” David Yeo, Ph.D. SAS Institute (Canada) Inc.

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Presentation on theme: "Copyright © 2011, SAS Institute Inc. All rights reserved. “I Don’t Need Enterprise Miner ” David Yeo, Ph.D. SAS Institute (Canada) Inc."— Presentation transcript:

1 Copyright © 2011, SAS Institute Inc. All rights reserved. “I Don’t Need Enterprise Miner ” David Yeo, Ph.D. SAS Institute (Canada) Inc.

2 2 Copyright © 2011, SAS Institute Inc. All rights reserved. Overview  The Case Against Using Enterprise Miner.  The Case For Using Enterprise Miner.  Questions.

3 3 Copyright © 2011, SAS Institute Inc. All rights reserved. The Case Against Using Enterprise Miner  The arguments for coding over using Enterprise Miner, are typified by the following statements:  “I like to code.”  “I don’t want to lose the time invested developing my code.”  “My code has proven reliable in past”.  “I understand what is going on in my code; I don’t fully understand what is going on in Enterprise Miner.”

4 4 Copyright © 2011, SAS Institute Inc. All rights reserved. The Case For Using Enterprise Miner  Intuitive “drag-and-drop” interface  Simplify tedious data preparation tasks.  Implement powerful advanced modeling techniques.  Integrate decision theory into your decisions.  Incorporate your favorite SAS programs and procedures.  Use Enterprise Miner as a code generator.

5 5 Copyright © 2011, SAS Institute Inc. All rights reserved. Intuitive “Drag-and-Drop” Interface  Sensible defaults facilitate rapid model construction.  Extensive documentation and context-sensitive help.

6 6 Copyright © 2011, SAS Institute Inc. All rights reserved. Simple Statistical Graphics  Offers an extensive range of plots including: histograms, scatterplots, contour plots, and even 3-D rotating plots.  Often the graphs are fully interconnected.

7 7 Copyright © 2011, SAS Institute Inc. All rights reserved. Automatic Design (Dummy) Coding...  Nominal and ordinal variables are automatically design (a.k.a. dummy) coded for use in subsequent models.  Either ‘effect’ or ‘reference cell’ coding can be specified.

8 8 Copyright © 2011, SAS Institute Inc. All rights reserved. Variable Selection  SAS Enterprise Miner offers an extensive set of variable selection methods: Sequential (stepwise) selection Split search selection Variable clustering R-square or chi-square based selection Variable importance in the projection

9 9 Copyright © 2011, SAS Institute Inc. All rights reserved. Missing Value Imputation  Synthetic (e.g. mean, mode).  Estimation (e.g. distribution, decision tree).

10 10 Copyright © 2011, SAS Institute Inc. All rights reserved. Variable Transformation  Simple (e.g. log) and advanced (e.g. optimal binning).

11 11 Copyright © 2011, SAS Institute Inc. All rights reserved. Association Analysis  Forms simultaneous or sequential associations. A B C A C D B C D A D E B C E RuleSupportConfidence A implies D2/52/3 C implies A2/52/4 A implies C2/52/3 B and C implies D1/51/3

12 12 Copyright © 2011, SAS Institute Inc. All rights reserved. Decision Trees  Enterprise Miner implements all of the major decision tree variants, i.e. CART, CHAID, and entropy-based.

13 13 Copyright © 2011, SAS Institute Inc. All rights reserved. Consolidation Trees x2x2 70% HI EFG x Level DADBDCDDDEDFDGDHDADBDCDDDEDFDGDH A B C D E F G H I  Combines categorical levels that have a similar outcome.

14 14 Copyright © 2011, SAS Institute Inc. All rights reserved. Neural Networks...  PROC NEURAL is one of SAS’ most powerful statistical procedures (it’s a universal approximator)!  Available neural network architectures include: MLP, RBF, VQ, SOM, and functional-link networks. hidden layer output layer input layer H1H1 H3H3 H2H2 Y x1x1 x2x2

15 15 Copyright © 2011, SAS Institute Inc. All rights reserved. Combined Models  Perturb and combine methodology (ensemble model).  Combine class probability model and continuous-valued prediction model (two-stage model). Combines predictions from multiple models to create a single consensus prediction.

16 16 Copyright © 2011, SAS Institute Inc. All rights reserved. Prior Probability  Enterprise Miner applies prior probability information to correct probability estimates for oversampling. Decision/Action Adjusted for Priors Actual Class 0 1 Decision/Action

17 17 Copyright © 2011, SAS Institute Inc. All rights reserved. Profit Matrix  The profit matrix sets the optimal decision cutoff value. ^ ^ p ≥ 0.68/15.82  solicit p < 0.68/15.82  ignore Bayesian optimal decision threshold 1 1      ≥ FPTN FNTP p ^  solicit

18 18 Copyright © 2011, SAS Institute Inc. All rights reserved. Conforming Profit 1/  0  If no profit matrix is available, use “conforming profit” to properly set the Bayesian optimal cutoff value    ≥ p ^  solicit where  1 is the population proportion of the primary event, and  0 is the proportion of the secondary event.

19 19 Copyright © 2011, SAS Institute Inc. All rights reserved. Adding SAS Programs  A SAS Code node can run any data step or licensed SAS procedure right within the data flow diagram.  This allows you to add SAS procedures and custom code not currently available as nodes in Enterprise Miner.  It also means you do not have to give up your favorite and familiar SAS programs and procedures! Your SAS code goes here.

20 20 Copyright © 2011, SAS Institute Inc. All rights reserved. Automated Model Assessment  Simultaneous assessment of multiple models using both statistical and graphical information.  Can assess models either on training or holdout data.  Offers a wide array of model selection options including: ASE, c-statistic (ROC index), and misclassification rate.

21 21 Copyright © 2011, SAS Institute Inc. All rights reserved. Enterprise Miner as a Code Generator  The entire data flow diagram can be output as:  Base SAS code (SAS/STAT is not required)  HTML code  C code

22 22 Copyright © 2011, SAS Institute Inc. All rights reserved. Questions  Contact Information: David Yeo, Ph.D. SAS Institute (Canada) Inc

23 Copyright © 2011, SAS Institute Inc. All rights reserved.


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