My Law
Upcoming SlideShare
Loading in...5
×
 

My Law

on

  • 423 views

tester

tester

Statistics

Views

Total Views
423
Views on SlideShare
423
Embed Views
0

Actions

Likes
0
Downloads
3
Comments
0

0 Embeds 0

No embeds

Accessibility

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

My Law My Law Presentation Transcript

  • Exercise Data Preparation
  • Modeling Example Business: National veterans’ organization Objective: From population of lapsing donors, identify individuals worth continued solicitation. Source: 1998 KDD-Cup Competition via UCI KDD Archive 2
  • The Story  A national veterans’ organization seeks to better target its solicitations for donation. By only soliciting the most likely donors, less money will be spent on solicitation efforts and more money will be available for charitable concerns.  Solicitations involve sending a small gift to an individual together with a request for donation. Gifts include mailing labels and greeting cards.  Of particular interest is the class of individuals identified as lapsing donors. These individuals made their most recent donation between 12 and 24 months ago. The organization found that by predicting the response behavior of this group, they can use the model to rank all 3.5 million individuals in their database.  The current campaign refers to a greeting card mailing sent in 06/1997.  The source of this data is the Association for Computing Machinery’s (ACM) 1998 KDD-Cup competition. 3
  • Additional Data Preparation The raw analysis data has been reduced for the purpose of this course. A subset of slightly over 19,000 records has been selected for modeling. As will be seen, this subset was not chosen arbitrarily. In addition, the 481 fields have been reduced to 50. Final Analysis Data Raw Analysis Data 19,372 Records 95,412 Records 50 Fields 481 Fields 4
  • Analysis Data Definition Donor master data CONTROL_NUMBER Unique Donor ID MONTHS_SINCE_ORIGIN Elapsed time since first donation IN_HOUSE 1=Given to In House program, 0=Not In House donor 5
  • Analysis Data Definition Demographic and other overlay data OVERLAY_SOURCE M=Metromail, P=Polk, B=both DONOR_AGE Age as of June 1997 DONOR_GENDER Actual or inferred gender PUBLISHED_PHONE Published telephone listing HOME_OWNER H=homeowner, U=unknown MOR_HIT Mail order response hit rate 6
  • Analysis Data Definition SES is a roll-up of the socio-economic field CLUSTER_CODE Demographic and other overlay data CLUSTER_CODE 54 Socio-economic cluster codes SES 5 Socio-economic cluster codes INCOME_GROUP 7 income group levels MED_HOUSEHOLD_INCOME Median income in $100s PER_CAPITA_INCOME Income per capita in dollars WEALTH_RATING 10 wealth rating groups 7
  • Analysis Data Definition Demographic and other overlay data MED_HOME_VALUE Median home value in $100s PCT_OWNER_OCCUPIED Percent owner occupied housing URBANICITY U=urban, C=city, S=suburban, T=town, R=rural, ?=unknown 8
  • Analysis Data Definition Census overlay data PCT_MALE_MILITARY Percent male military in block PCT_MALE_VETERANS Percent male veterans in block PCT_VIETNAM_VETERANS Percent Vietnam veterans in block PCT_WWII_VETERANS Percent WWII veterans in block 9
  • Analysis Data Definition Transaction detail data NUMBER_PROM_12 Number promotions last 12 mos. CARD_PROM_12 Number card promotions last 12 mos. 97NK Time `94 `95 `96 `97 `98 10
  • Analysis Data Definition Transaction detail data FREQ_STATUS_97NK Frequency status, June `97 RECENCY_STATUS_96NK Recency status, June `96 MONTHS_SINCE_LAST Months since last donation LAST_GIFT_AMT Amount of most recent donation 96NK 97NK Time `94 `95 `96 `97 `98 11
  • Analysis Data Definition The sampling method implies that no one made a donation between 6/1996 and 6/1997. However, for a limited number of cases, the number of months since last gift is fewer than 12. This contradiction is not resolved in the data’s documentation, nor will it be resolved here. RECENT transaction detail data RESPONSE_PROP Response proportion since June `94 RESPONSE_COUNT Response count since June `94 AVG_GIFT_AMT Average gift amount since June `94 RECENT_STAR_STATUS STAR (1, 0) status since June `94 94NK 96NK Time `94 `95 `96 `97 `98 12
  • Analysis Data Definition RECENT transaction detail data CARD_RESPONSE_PROP Response proportion since June `94 CARD_RESPONSE_COUNT Response count since June `94 CARD_AVG_GIFT_AMT Average gift amount since June `94 94NK 96NK Time `94 `95 `96 `97 `98 13
  • Analysis Data Definition LIFETIME transaction detail data PROM Total number promotions ever GIFT_COUNT Total number donations ever AVG_GIFT_AMT Overall average gift amount PEP_STAR STAR status ever (1=yes, 0=no) 94NK 96NK Time `94 `95 `96 `97 `98 14
  • Analysis Data Definition LIFETIME transaction detail data GIFT_AMOUNT Total gift amount ever GIFT_COUNT Total number donations ever MAX_GIFT Maximum gift amount GIFT_RANGE Maximum less minimum gift amount 94NK 96NK Time `94 `95 `96 `97 `98 15
  • Analysis Data Definition KDD supplied LIFETIME transaction detail data FILE_AVG_GIFT Average gift from raw data FILE_CARD_GIFT Average card gift raw data MONTHS_SINCE_FIRST First donation date from June `97 MONTHS_SINCE_LAST Last donation date from June `97 94NK 96NK Time `94 `95 `96 `97 `98 16
  • Analysis Data Definition Transaction detail data target definition TARGET_B Response to 97NK solicitation (1=yes 0=no) TARGET_D Response amount to 97NK solicitation (missing if no response) 97NK Time `94 `95 `96 `97 `98 17
  • Demonstration Data set: PVA_RAW_DATA Purpose: Get familiar with the data  Basic decision modeling with tree, regression, and neural network  Parameters: Prior probabilities: (0.05, 0.95)  Profit matrix: ($14.62, -0.68)  Target: TARGET_B (TARGET_D must be rejected)  18
  • Improving Regression Selection 60 All Subsets 45 Minutes 30 Stepwise 15 0 25 50 75 100 Number of Variables 19
  • Improving Input Selection  Much of the success of a predictive model depends on input selection. Most input selection processes attempt to minimize input redundancy and maximize input relevancy.  Selection is usually using a heuristic search because the complexity of an exhaustive (all subsets) search increases exponentially in the number of inputs.  There exist branch-and-bound algorithms that approximate an exhaustive input search and run quite quickly for a reasonably small number of inputs. One algorithm, found in the SAS/STAT LOGISTIC procedure, actually runs faster than the usual forward, backward, and stepwise procedures.  While the example data set in this course has fewer than 60 inputs, many modeling data sets do not. Given the promise of an exhaustive search, it would be extremely desirable to reduce the input count without compromising the quality of the ultimate predictive model. 20
  • Improving Input Selection Univariate Screening Variable Clustering Categorical Recoding All Subsets Selection 21
  • Input Dimension Reduction  A three-phased approach is proposed for input dimension reduction in preparation for all subsets selection.  First, a univariate screening is performed to eliminate those inputs with little promise of target association. This must be done with care to avoid eliminating inputs whose predictive value occurs only in conjunction with other inputs.  Second, variable clustering techniques are used to group correlated interval inputs and minimize input redundancy.  Third, enhanced weight-of-evidence methods are used to effectively incorporate categorical inputs into the final model.  With the input dimension reduced, an all subsets search commences on the remaining inputs. 22
  • Univariate Screening  In this technique, inputs are screened based on their individual correlation with the target and only the inputs with the highest correlations are kept.  Unfortunately, this approach does not account for partial associations among the inputs. Inputs could be erroneously omitted or erroneously included. Partial associations occur when the effect of one input changes in the presence of another input.  A compromise devised to minimize the dangers of partial associations is to use univariate screening followed by liberal forward selection—not as a way of finding useful inputs, but rather as a way to eliminate clearly useless ones. 23
  • R-square Selection for Univariate Screening The R-square selection approach has two phases.  First, the input/target correlation is calculated for each input. Each input with a correlation below the minimum R-square setting is rejected.  Second, a forward election is performed. The forward selection procedure terminates when all remaining inputs have a correlation below the specified stop R- square. These remaining inputs are also rejected. 24