SYSTEMS AND METHODS FOR MACHINE LEARNING FEATURE GENERATION
A method and apparatus for generating an ML model may include: generating an ML feature template comprising a first grouping of first ML feature variables and a second grouping of second ML feature variables; generating ML features by combining a respective one of each of the first ML feature variables with a respective one of each of the second ML feature variables; training a first ML model utilizing the ML features and first training data to generate an ML output; analyzing the ML output to determine a prediction accuracy of the ML features; based on the prediction accuracy of the ML features, selecting a subset of the ML features; training a second ML model based on the subset of the ML features and the first training data; and providing a network transaction to the second ML model to generate a classification of the network transaction.
1 . A method for generating a machine learning model comprising:
generating a machine learning feature template, the machine learning feature template comprising a first grouping of first machine learning feature variables and a second grouping of second machine learning feature variables;
generating a plurality of machine learning features by combining a respective one of each of the first machine learning feature variables of the first grouping with a respective one of each of the second machine learning feature variables of the second grouping;
training a first machine learning model utilizing the plurality of machine learning features and first training data to generate a machine learning output;
analyzing the machine learning output to determine a prediction accuracy of the plurality of machine learning features;
based on the prediction accuracy of the plurality of machine learning features, selecting a subset of the plurality of machine learning features;
training a second machine learning model based on the subset of the plurality of machine learning features and the first training data; and
providing a network transaction to the second machine learning model to generate a classification of the network transaction.
2 . The method of claim 1 , further comprising:
based on the prediction accuracy of the plurality of machine learning features, modifying at least one of the first grouping of the first machine learning feature variables or the second grouping of the second machine learning feature variables of the machine learning feature template to generate a modified machine learning feature template;
obtaining second training data;
generating a second plurality of machine learning features based on the modified first and second groupings of the modified machine learning feature template; and
training a third machine learning model using the second plurality of machine learning features and the second training data.
3 . The method of claim 1 , wherein the first grouping of machine learning feature variables comprises a plurality of characteristics associated with a plurality of network transactions and the second grouping of the machine learning feature variables comprises a plurality of categories into which one or more of the characteristics are to be grouped.
4 . The method of claim 1 wherein the machine learning feature template is a first machine learning feature template of a plurality of machine learning feature templates,
wherein the first machine learning feature template is associated with a first prediction category, and
wherein the method further comprises:
obtaining second training data associated with a second prediction category;
selecting the first machine learning feature template based on the second prediction category matching the first prediction category,
generating a second plurality of machine learning features by combining a respective one of each of the first machine learning feature variables of the first grouping with a respective one of each of the second machine learning feature variables of the second grouping; and
training a third machine learning model utilizing one or more of the second plurality of machine learning features and the second training data.
5 . The method of claim 4 , wherein the first training data comprises a first plurality of transactions for a first merchant and the second training data comprises a second plurality of transactions for a second merchant, and
wherein selecting the first machine learning feature template is further based on a comparison of a first profile of the first merchant and a second profile of the second merchant.
6 . The method of claim 4 , further comprising:
prior to generating the second plurality of machine learning features, modifying at least one of the first machine learning feature variables of the first grouping or the second machine learning feature variables of the second grouping based on characteristics of the second training data.
7 . The method of claim 1 , wherein the network transaction is one of a plurality of network transactions, and
wherein the second machine learning model is configured to predict fraudulent transactions in the plurality of network transactions.
8 . A non-transitory computer readable storage medium including instructions that, when executed by a processor, cause the processor to perform operations for generating a machine learning model, the operations comprising:
generating a machine learning feature template, the machine learning feature template comprising a first grouping of first machine learning feature variables and a second grouping of second machine learning feature variables;
generating a plurality of machine learning features by combining a respective one of each of the first machine learning feature variables of the first grouping with a respective one of each of the second machine learning feature variables of the second grouping;
training a first machine learning model utilizing the plurality of machine learning features and first training data to generate a machine learning output;
analyzing the machine learning output to determine a prediction accuracy of the plurality of machine learning features;
based on the prediction accuracy of the plurality of machine learning features, selecting a subset of the plurality of machine learning features;
training a second machine learning model based on the subset of the plurality of machine learning features and the first training data; and
providing a network transaction to the second machine learning model to generate a classification of the network transaction.
9 . The non-transitory computer readable storage medium according to claim 8 , wherein the operations further comprise:
based on the prediction accuracy of the plurality of machine learning features, modifying at least one of the first grouping of the first machine learning feature variables or the second grouping of the second machine learning feature variables of the machine learning feature template to generate a modified machine learning feature template;
obtaining second training data;
generating a second plurality of machine learning features based on the modified first and second groupings of the modified machine learning feature template; and
training a third machine learning model using the second plurality of machine learning features and the second training data.
10 . The non-transitory computer readable storage medium according to claim 8 , wherein the first grouping of machine learning feature variables comprises a plurality of characteristics associated with a plurality of network transactions and the second grouping of the machine learning feature variables comprises a plurality of categories into which one or more of the characteristics are to be grouped.
11 . The non-transitory computer readable storage medium according to claim 8 , wherein the machine learning feature template is a first machine learning feature template of a plurality of machine learning feature templates,
wherein the first machine learning feature template is associated with a first prediction category, and
wherein the operations further comprise:
obtaining second training data associated with a second prediction category;
selecting the first machine learning feature template based on the second prediction category matching the first prediction category,
generating a second plurality of machine learning features by combining a respective one of each of the first machine learning feature variables of the first grouping with a respective one of each of the second machine learning feature variables of the second grouping; and
training a third machine learning model utilizing one or more of the second plurality of machine learning features and the second training data.
12 . The non-transitory computer readable storage medium according to claim 11 , wherein the first training data comprises a first plurality of transactions for a first merchant and the second training data comprises a second plurality of transactions for a second merchant, and
wherein selecting the first machine learning feature template is further based on a comparison of a first profile of the first merchant and a second profile of the second merchant.
13 . The non-transitory computer readable storage medium according to claim 11 , wherein the operations further comprise:
prior to generating the second plurality of machine learning features, modifying at least one of the first machine learning feature variables of the first grouping or the second machine learning feature variables of the second grouping based on characteristics of the second training data.
14 . The non-transitory computer readable storage medium according to claim 8 , wherein the network transaction is one of a plurality of network transactions, and
wherein the second machine learning model is configured to predict fraudulent transactions in the plurality of network transactions.
15 . A commerce platform system for generating a machine learning model, comprising:
a memory; and
a processor coupled with the memory configured to:
generate a machine learning feature template, the machine learning feature template comprising a first grouping of first machine learning feature variables and a second grouping of second machine learning feature variables;
generate a plurality of machine learning features by combining a respective one of each of the first machine learning feature variables of the first grouping with a respective one of each of the second machine learning feature variables of the second grouping;
train a first machine learning model utilizing the plurality of machine learning features and first training data to generate a machine learning output;
analyze the machine learning output to determine a prediction accuracy of the plurality of machine learning features;
based on the prediction accuracy of the plurality of machine learning features, select a subset of the plurality of machine learning features;
train a second machine learning model based on the subset of the plurality of machine learning features and the first training data; and
provide a network transaction to the second machine learning model to generate a classification of the network transaction.
16 . The commerce platform system according to claim 15 , wherein the processor is further configured to:
based on the prediction accuracy of the plurality of machine learning features, modify at least one of the first grouping of the first machine learning feature variables or the second grouping of the second machine learning feature variables of the machine learning feature template to generate a modified machine learning feature template;
obtain second training data;
generate a second plurality of machine learning features based on the modified first and second groupings of the modified machine learning feature template; and
train a third machine learning model using the second plurality of machine learning features and the second training data.
17 . The commerce platform system according to claim 15 , wherein the first grouping of machine learning feature variables comprises a plurality of characteristics associated with a plurality of network transactions and the second grouping of the machine learning feature variables comprises a plurality of categories into which one or more of the characteristics are to be grouped.
18 . The commerce platform system according to claim 15 , wherein the machine learning feature template is a first machine learning feature template of a plurality of machine learning feature templates,
wherein the first machine learning feature template is associated with a first prediction category, and
wherein the processor is further configured to:
obtain second training data associated with a second prediction category;
select the first machine learning feature template based on the second prediction category matching the first prediction category,
generate a second plurality of machine learning features by combining a respective one of each of the first machine learning feature variables of the first grouping with a respective one of each of the second machine learning feature variables of the second grouping; and
train a third machine learning model utilizing one or more of the second plurality of machine learning features and the second training data.
19 . The commerce platform system according to claim 18 , wherein the first training data comprises a first plurality of transactions for a first merchant and the second training data comprises a second plurality of transactions for a second merchant, and
wherein selecting the first machine learning feature template is further based on a comparison of a first profile of the first merchant and a second profile of the second merchant.
20 . The commerce platform system according to claim 18 , wherein the processor is further configured to:
prior to generating the second plurality of machine learning features, modifying at least one of the first machine learning feature variables of the first grouping or the second machine learning feature variables of the second grouping based on characteristics of the second training data.