Predictor Schema Example
This example shows all the related tables and entries for a model configuration named PurchaseCfg1.
PredictorModelCfgs
| ModelCfgName | SiteName |
| PurchaseCfg1 | Retail |
This example assumes that the PurchaseCfg1 model configuration consists of three sources of data:
Demographic information from the User table (additional rows not shown).
| UserID | Age | Gender | Education |
| jilluser@roguecellars.com | 23 | F | 19 |
| barneyuser@arborshoes.com | 49 | M | 3 |
| jackuser@frogkick.com | 28 | M | 13 |
Product Purchase information from the Purchases table (additional rows not shown).
| UserID | SKU | QTY |
| jilluser@roguecellars.com | wine_fine_au_zin_19 | 3 |
| jilluser@roguecellars.com | tv_big_36_m3 | 1 |
| jilluser@roguecellars.com | pumps_bl_6 | 1 |
Ad Click information from the AdClicks table (additional rows not shown).
| UserID | URL | Count |
| jilluser@roguecellars.com | www.thewinecellar.com/ad_zin12 | 3 |
| jilluser@roguecellars.com | www.thewinecellar.com/ad_cab3 | 2 |
| jilluser@roguecellars.com | www.arborshoes.com/ad4c | 1 |
PredictorDataTables
| ModelCfgName | TableName | Type | Case Column |
Pivot Column |
Aggregate Column |
Aggregate Type |
| PurchaseCfg1 | User | 0 | UserID | <NULL> | <NULL> | <NULL> |
| PurchaseCfg1 | Purchases | 1 | UserID | SKU | QTY | 0 |
| PurchaseCfg1 | AdClicks | 1 | UserID | URL | Count | 0 |
| PurchaseCfg1 | PurchaseCfg1_Attributes | 2 | Not applicable | <NULL> | <NULL> | <NULL> |
The entries in the following table override the default behavior. The first three entries construct a hierarchy where "tv_big_36_m3" is the child of "TVs" and "TVs" is the child of "Electronic Devices". "Electronic Devices" stands at the top of the hierarchy and therefore does not have a parent. The fourth entry specifies all of the SKUs to have a Discrete and Modeled As Binary distribution. The fifth row specifies to use the Age attribute to predict other attributes, but not to be predicted. This means that no Decision Tree predicting the Age attribute will be built. The entry also specifies the Distribution to be Continuous, Lognormal and not Modeled As Binary.
PredictorAttributes_PurchaseCfg1
| Prop ID |
Parent ID |
Name | Table Name |
Column Name |
Distri- bution |
UseTo Predict |
Predict |
| 1 | 0 | Electronic Devices | <NULL> | <NULL> | <NULL> | <NULL> | <NULL> |
| 2 | 1 | TVs | <NULL> | <NULL> | <NULL> | <NULL> | <NULL> |
| 3 | 2 | tv_big_36_m3 | <NULL> | <NULL> | 4 | True | True |
| 4 | 0 | SKU | <NULL> | <NULL> | 4 | True | True |
| 5 | 0 | Age | <NULL> | <NULL> | 2 | True | False |
The following table shows two models built from the PurchaseCfg1 model configuration. The first model, Purchase1, was built as a prediction (type 0) model. The second model, Demog1, was built as a segment (type 1) model.
PredictorModels
| Model Name |
ModelCfg Name |
Model Type |
Date Created |
Build Time |
Measured Accuracy Sample Fraction |
Measured Accuracy Max Predictions |
K |
| Purchase1 | PurchaseCfg1 | 0 | getdate() | 30000 | .05 | 10 | <NULL> |
| Demog1 | PurchaseCfg1 | 1 | getdate() | 45000 | .05 | 10 | 10 |
(PredictorModels continued)
| Max Buffer Size |
Input Attribute Fraction |
Output Attribute Fraction |
Sample Size |
Complexity Penality |
Minimum CasesTo Split |
Data Fit Score |
Recommend Score |
Data |
| <NULL> | 1 | 1 | -1 | 100 | 5 | 11.08 | 0.3946 | <Binary> |
| 1000000 | 1 | 1 | 16000 | <NULL> | <NULL> | 4.177 | 0.1874 | <Binary> |