Methods and systems for identifying breakpoints in variable impact on model results
Techniques described herein relate to a method for identifying breakpoints. The method may include obtaining, by a breakpoint identification device, an entity group data set corresponding to an entity group; generating, using the entity group data set, an enhanced entity group data set comprising the entity group data set and a derived data item; performing a clustering analysis using at least a portion of the enhanced entity group data set to obtain a plurality of entity clusters; training a machine learning (ML) model using the enhanced entity group data set to obtain a trained ML model; using the trained ML model and a set of simulated entities to perform a breakpoint analysis; generating a breakpoint graph based on the breakpoint analysis; and providing recommendations to an interested entity based on a breakpoint identified using the breakpoint graph.
1. A method for identifying breakpoints, the method comprising:
obtaining, by a breakpoint identification device, an entity group data set corresponding to an entity group;
generating, using the entity group data set, an enhanced entity group data set comprising the entity group data set and a derived data item, wherein each enhanced entity of the enhanced entity group data set comprises a set of input variables;
performing a clustering analysis using at least a portion of the enhanced entity group data set to obtain a plurality of entity clusters;
training a machine learning (ML) model using the enhanced entity group data set to obtain a trained ML model;
using the trained ML model to determine a relative importance of the set of input variables for the trained ML model;
reducing a quantity of input variables by:
removing at least one input variable of the set of input variables based on the relative importance to obtain a first reduced set of input variables;
training the ML model using the first reduced set of input variables to obtain a first reduced trained ML model;
making a first determination that the first reduced trained ML model has a first prediction accuracy above a threshold;
removing at least one input variable of the first reduced set of input variables based on the relative importance to obtain a second reduced set of input variables;
training the first reduced ML model using the second reduced set of input variables to obtain a second reduced trained ML model; and
making a second determination that the second reduced trained ML model has a second prediction accuracy above the threshold;
generating, using the enhanced entity group data set, a set of simulated entities,
wherein each simulated entity of the set of simulated entities comprises a modified variable of the set of important input variables;
inputting the set of simulated entities into the reduced trained ML model to perform a breakpoint analysis;
generating a breakpoint graph based on the breakpoint analysis; and
providing recommendations to an interested entity based on a breakpoint identified using the breakpoint graph.
2. The method of claim 1 , wherein the modified variable is online transactions as a percentage of total transactions.
3. The method of claim 1 , wherein an additional variable of each simulated entity of the set of simulated entities is modified based on a value of the modified variable.
4. The method of claim 3 , wherein the additional variable of each simulated entity of the set of simulated entities is modified based at least in part on a scatterplot comprising the modified variable and the additional variable.
5. The method of claim 1 , wherein the recommendations correspond to an entity cluster of the plurality of entity clusters.
6. The method of claim 1 , wherein the trained ML model predicts sales revenue for a sales representative.
7. A non-transitory computer readable medium comprising computer readable program code, which when executed by a computer processor enables the computer processor to perform a method for identifying breakpoints, the method comprising:
obtaining, by a breakpoint identification device, an entity group data set corresponding to an entity group;
generating, using the entity group data set, an enhanced entity group data set comprising the entity group data set and a derived data item, wherein each enhanced entity of the enhanced entity group data set comprises a set of input variables;
performing a clustering analysis using at least a portion of the enhanced entity group data set to obtain a plurality of entity clusters;
training a machine learning (ML) model using the enhanced entity group data set to obtain a trained ML model;
using the trained ML model to determine a relative importance of the set of input variables for the trained ML model;
reducing a quantity of input variables by:
removing at least one input variable of the set of input variables based on the relative importance to obtain a first reduced set of input variables;
training the ML model using the first reduced set of input variables to obtain a first reduced trained ML model;
making a first determination that the first reduced trained ML model has a first prediction accuracy above a threshold;
removing at least one input variable of the first reduced set of input variables based on the relative importance to obtain a second reduced set of input variables;
training the first reduced ML model using the second reduced set of input variables to obtain a second reduced trained ML model; and
making a second determination that the second reduced trained ML model has a second prediction accuracy above the threshold;
generating, using the enhanced entity group data set, a set of simulated entities,
wherein each simulated entity of the set of simulated entities comprises a modified variable of the set of important input variables;
inputting the set of simulated entities into the reduced trained ML model to perform a breakpoint analysis;
generating a breakpoint graph based on the breakpoint analysis; and
providing recommendations to an interested entity based on a breakpoint identified using the breakpoint graph.
8. The non-transitory computer readable medium of claim 7 , wherein the modified variable is online transactions as a percentage of total transactions.
9. The non-transitory computer readable medium of claim 7 , wherein an additional variable of each simulated entity of the set of simulated entities is modified based on a value of the modified variable.
10. The non-transitory computer readable medium of claim 9 , wherein the additional variable of each simulated entity of the set of simulated entities is modified based at least in part on a scatterplot comprising the modified variable and the additional variable.
11. The non-transitory computer readable medium of claim 7 , wherein the recommendations correspond to an entity cluster of the plurality of entity clusters.
12. The non-transitory computer readable medium of claim 7 , wherein the trained ML model predicts sales revenue for a sales representative.
13. A system for identifying breakpoints, the system comprising:
a breakpoint identification device, executing on a processor comprising circuitry, and configured to:
obtain, by a breakpoint identification device, an entity group data set corresponding to an entity group;
generate, using the entity group data set, an enhanced entity group data set comprising the entity group data set and a derived data item, wherein each enhanced entity of the enhanced entity group data set comprises a set of input variables;
perform a clustering analysis using at least a portion of the enhanced entity group data set to obtain a plurality of entity clusters;
train a machine learning (ML) model using the enhanced entity group data set to obtain a trained ML model;
use the trained ML model to determine a relative importance of the set of input variables for the trained ML model;
reducing a quantity of input variables by:
removing at least one input variable of the set of input variables based on the relative importance to obtain a first reduced set of input variables:
training the ML model using the first reduced set of input variables to obtain a first reduced trained ML model;
making a first determination that the first reduced trained ML model has a first prediction accuracy above a threshold;
removing at least one input variable of the first reduced set of input variables based on the relative importance to obtain a second reduced set of input variables;
training the first reduced ML model using the second reduced set of input variables to obtain a second reduced trained ML model; and
making a second determination that the second reduced trained ML model has a second prediction accuracy above the threshold;
generate, using the enhanced entity group data set, a set of simulated entities,
wherein each simulated entity of the set of simulated entities comprises a modified variable of the set of important input variables;
input the set of simulated entities into the reduced trained ML model and a set of simulated entities to perform a breakpoint analysis;
generate a breakpoint graph based on the breakpoint analysis; and
provide recommendations to an interested entity based on a breakpoint identified using the breakpoint graph.
14. The system of claim 13 , wherein:
the modified variable is online transactions as a percentage of total transactions, and
an additional variable of each simulated entity of the set of simulated entities is modified based on a value of the modified variable.
15. The system of claim 13 , wherein the trained ML model predicts sales revenue for a sales representative.