Scalable Predictive Analytic System
A system for validating models for predicting a client behavior event includes a development module and a validation module. The development module is configured to receive a use case corresponding to the client behavior event and select a subset of variables correlated to the client behavior event. The validation module is configured to select a first model from models that predict client behavior event using the selected subset of variables. The development module selects the first model based on a predicted lift of the first model. The validation module applies the first model to client data acquired subsequent to the selection of the first model. The validation module compares the predicted lift of the first model to an actual lift of the first model as applied to the client data. The validation module selects one of the first model and a different model in response to the comparison.
1 . A system for validating models for predicting a client behavior event, the system comprising:
a development module configured to
receive a use case corresponding to the client behavior event, and
select a subset of variables correlated to the client behavior event; and
a validation module configured to
select a first model from a plurality of models, wherein each of the plurality of models is configured to predict the client behavior event using the selected subset of variables, and wherein the development module is configured to select the first model based on a predicted lift of the first model,
apply the first model to client data acquired subsequent to the selection of the first model,
compare the predicted lift of the first model to an actual lift of the first model as applied to the client data, and
select one of the first model and a different one of the plurality of models in response to the comparison between the predicted lift of the first model and the actual lift of the first model as applied to the client data.
2 . The system of claim 1 , wherein the client behavior event corresponds to client attrition.
3 . The system of claim 1 , wherein receiving the use case includes receiving the use case from a user device.
4 . The system of claim 1 , wherein selecting the subset of variables includes applying a plurality of variable selection algorithms to the client data.
5 . The system of claim 1 , wherein the validation module is further configured to verify stability of the selected model.
6 . The system of claim 1 , wherein the development module is configured to select a subset of variables correlated to the client behavior event in response to an input received from a user device.
7 . The system of claim 1 , wherein the development module is configured to modify non-selected ones of the plurality of models based on the first model.
8 . The system of claim 1 , wherein the validation module is configured to select the first model from the plurality of models by (i) performing cross-validation of the plurality of models to determine respective lifts of the plurality of models and (ii) selecting the first model based on the respective lifts of the plurality of models.
9 . The system of claim 8 , wherein the validation module is configured to perform cross-validation of the plurality of models subsequent to selecting the first model and in accordance with client data acquired subsequent to selecting the first model.
10 . The system of claim 9 , wherein the validation module is configured to select a second model from the plurality of models based on the cross-validation of the plurality of models performed subsequent to selecting the first model.
11 . A method for validating models for predicting a client behavior event, the method comprising:
using a computing device:
receiving a use case corresponding to the client behavior event;
selecting a subset of variables correlated to the client behavior event;
selecting a first model from a plurality of models, wherein each of the plurality of models is configured to predict the client behavior event using the selected subset of variables, and wherein the first model is selected based on a predicted lift of the first model;
applying the first model to client data acquired subsequent to the selection of the first model;
comparing the predicted lift of the first model to an actual lift of the first model as applied to the client data; and
selecting one of the first model and a different one of the plurality of models in response to the comparison between the predicted lift of the first model and the actual lift of the first model as applied to the client data.
12 . The method of claim 11 , wherein the client behavior event corresponds to client attrition.
13 . The method of claim 11 , wherein receiving the use case includes receiving the use case from a user device.
14 . The method of claim 11 , wherein selecting the subset of variables includes applying a plurality of variable selection algorithms to the client data.
15 . The method of claim 11 , further comprising providing the selected subset of variables to a user device.
16 . The method of claim 11 , further comprising selecting a subset of variables correlated to the client behavior event in response to an input received from a user device.
17 . The method of claim 11 , further comprising modifying non-selected ones of the plurality of models based on the selected first model.
18 . The method of claim 11 , further comprising (i) performing cross-validation of the plurality of models to determine respective lifts of the plurality of models and (ii) selecting the first model based on the respective lifts of the plurality of models.
19 . The method of claim 18 , further comprising performing cross-validation of the plurality of models subsequent to selecting the first model and in accordance with client data acquired subsequent to selecting the first model.
20 . The method of claim 19 , further comprising selecting a second model from the plurality of models based on the cross-validation of the plurality of models performed subsequent to selecting the first model.