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US 2022/0067460 A1Published application
Variance Characterization Based on Feature Contribution
AssigneeCapital One Services, LLC
InventorsRohan Vimal Raj, Varun Parameswaran, Jie Dai, Aaron Osborne
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Abstract
Systems, methods, and computer readable media are disclosed for generating, modifying, and using machine learning models to predict and evaluate variances between data sets. Methods disclosed herein may include identifying features that characterize members of a data set, generating a machine learning model using identified features, using the machine learning model and the group to assign feature attributions to the features, and predicting the impact of those features on behaviors of the first data set and a second data set.
Claims20
A computer-implemented method for generating and utilizing a machine learning model for variance characterization between data sets, the method comprising:
- retrieving a first data snapshot and a second data snapshot of a data set, wherein the data set includes a plurality of members and wherein each member of the plurality of members in the first data snapshot includes a first plurality of data points and each member of the plurality of members in the second data snapshot includes a second plurality of data points ;
- applying a first label to each data point of the first plurality of data points in the first data snapshot;
- applying a second label to each data point of the second plurality of data points in the second data snapshot;
- combining the first data snapshot and the second data snapshot to form a combined data set;
- identifying, from the combined data set, first data representing a first number of data points and second data representing a second number of data points, wherein the first data is identified based on the first label, the second data is identified based on the second label, and the first number is equal to the second number;
- forming a rebalanced data set including a first balanced number of data points from the first data and a second balanced number of data points from the second data;
- fitting the machine learning model to the rebalanced data set to generate a fitted machine learning model;
- and
- determining, based on the fitted machine learning model, a performance change between the first data snapshot and the second data snapshot.
6 more elements
A non-transitory computer-readable medium storing instructions, the instructions, when executed by a processor, cause the processor to perform operations comprising:
- retrieving a first data snapshot and a second data snapshot of a data set, wherein the data set includes a plurality of members and wherein each member of the plurality of members in the first data snapshot includes a first plurality of data points and each member of the plurality of members in the second data snapshot includes a second plurality of data points;
- determining a population shift between the first data snapshot and the second data snapshot;
- applying a first label to each data point of the first plurality of data points in the first data snapshot;
- applying a second label to each data point of the second plurality of data points in the second data snapshot, wherein the first label and the second label differentiate each data point in the first data snapshot from each data point in the second snapshot;
- combining the first data snapshot and the second data snapshot to form a combined data set;
- identifying, from the combined data set, first data representing a first number of data points and second data representing a second number of data points, wherein the first data is identified based on the first label and the first number is equal to the second number;
- forming, based on the population shift, a rebalanced data set including a first balanced number of data points from the first data and a second balanced number of data points from the second data;
- fitting a machine learning model to the rebalanced data set to generate a fitted machine learning model;
- and
- determining a performance change between the first data snapshot and the second data snapshot using the fitted machine learning model.
7 more elements
A computer-implemented method for generating and utilizing a machine learning model for characterizing changes between data sets:
- a memory;
- and
- a processor communicatively coupled to the memory and configured to:
- retrieve a first data snapshot and a second data snapshot of a data set, wherein the data set includes a plurality of members and wherein each member of the plurality of members in the first data snapshot includes a first plurality of data points and each member of the plurality of members in the second data snapshot includes a second plurality of data points;
- applying a first label to each data point of the first plurality of data points in the first data snapshot;
- applying a second label to each data point of the second plurality of data points in the second data snapshot;
- combining the first data snapshot and the second data snapshot to form a combined data set;
- identifying, from the combined data set, first data representing a first number of data points and second data representing a second number of data points, wherein the first data is identified based on the first label and the first number is equal to the second number;
- forming a rebalanced data set including a first balanced number of data points from the first data and a second balanced number of data points from the second data;
- fitting a machine learning model to the rebalanced data set to generate a fitted machine learning model;
- determining, using the fitted machine learning model, a performance change between the first data snapshot and the second data snapshot associated with the plurality of features;
- and
- generating a waterfall chart indicating the performance change.
10 more elements
Dependencies are inferred from the claim text.