US 12,770,051 B2Granted

Systems and methods for machine learning interpretability

AssigneeKinaxis Inc.

InventorsBehrouz Haji Soleimani, Andrea Pagotto, Seyednaser Nourashrafeddin, Chantal Bisson-Krol

Life of this patent

Estimating the term…

Abstract

Methods and systems that provide machine learning interpretability. SHAP values of historical and predicted data, along with features of both, are used to provide a measure of the impact of training data points on a predictions. Removal of an individual training data point from a training data set, followed by comparing the resulting prediction with that obtained by the full training data set, also provides a measure of influence of individual training data points on forecasts.

Claims18

Dependent claims

A method comprising:

training, by a processor, a regression machine learning model using training data comprising training data points for a historical time-series;
utilizing, by the processor, the trained regression machine learning model to generate a prediction;
receiving, by a machine learning interpretability module, the training data, the trained model and the prediction;
determining, for each of the training data points of the training data, a measure of influence for each of the training data points, wherein a procedure for determining the measure of influence for each of the training data points comprises:
a) removing, by the machine learning interpretability module, the training data point from the training data to form an amended training data set;
b) retraining, by the machine learning interpretability module, the trained regression machine learning model on the amended training data set;
c) utilizing, by the machine learning interpretability module, the retrained regression machine learning model to generate an amended prediction;
d) determining, by the machine learning interpretability module, a difference between the prediction generated by the trained regression machine learning model and the amended prediction generated by the retrained regression machine learning model;
e) determining, by the machine learning interpretability module, a measure of influence for the removed training data point, based on the difference;
f) return the removed training data point to the amended training data set along with the measure of influence for the removed training data point;
g) remove, by the machine learning interpretability module, another training data point from the training data to form another amended training data set;
and
repeat the procedure a)-g) for determining and returning the measure of influence for each of the training data points in the training data;
and
visually outputting the measure of influence of each of the training data points to a graphical user interface so that a visualization of the training data comprises a visualization of the measure of influence of each of the training points on the prediction.

12 more elements

A system comprising:

a processor;
and
a memory storing instructions that, when executed by the processor, configure the system to:
train, by a processor, a regression machine learning model using training data comprising training data points for a historical time-series;
utilize, by the processor, the trained regression machine learning model to generate a prediction;
receive, by a machine learning interpretability module, the training data, the trained model and the prediction;
determine, for each of the training data points of the training data, a measure of influence for the training data points, wherein a procedure for determining the measure of influence for each of the one or more training data points comprises:
a) remove, by the machine learning interpretability module, a first training data point from the training data to form an amended training data set;
b) retrain, by the machine learning interpretability module, the trained regression machine learning model on the amended training data set;
c) utilize, by the machine learning interpretability module, the retrained regression machine learning model to generate an amended prediction;
d) determine, by the machine learning interpretability module, a difference between the prediction generated by the trained regression machine learning model and the amended prediction generated by the retrained regression machine learning model;
e) determine, by the machine learning interpretability module, a measure of influence for the removed first training data point, based on the difference;
f) return the removed first training data point to the amended training data set along with the measure of influence for the removed training data point;
g) remove, by the machine learning interpretability module, another training data point from the training data to form another amended training data set;
and
repeat the procedure a)-g) for determining and returning the measure of influence for each of the training data points in the training data;
and
visually output the measure of influence to the one or more training data points to a graphical user interface so that a visualization of the training data comprises a visualization of the measure of influence of each of the training points on the prediction.

15 more elements

A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:

train, by a processor, a regression machine learning model using training data comprising training data points for a historical time-series;
utilize, by the processor, the trained regression machine learning model to generate a prediction;
receive, by a machine learning interpretability module, the training data, the trained model and the prediction;
determine, for each of the training data points of the training data, a measure of influence for each of the training data points, wherein a procedure determining the measure of influence for each of the training data points comprises:
a) remove, by the machine learning interpretability module, the training data point from the training data to form an amended training data set;
b) retrain, by the machine learning interpretability module, the trained regression machine learning model on the amended training data set;
c) utilize, by the machine learning interpretability module, the retrained regression machine learning model to generate an amended prediction;
d) determine, by the machine learning interpretability module, a difference between the prediction generated by the trained regression machine learning model and the amended prediction generated by the retrained regression machine learning model;
e) determine, by the machine learning interpretability module, a measure of influence for the removed training data point, based on the difference;
f) return the removed training data point to the amended training data set along with the measure of influence for the removed training data point;
and
g) remove, by the machine learning interpretability module, another training data point from the training data to form another amended training data set;
and
repeat the procedure a)-g) for determining and returning the measure of influence for each of the training data points in the training data;
and
visually output the measure of influence of the one or more training data points to a graphical user interface so that a visualization of the training data comprises a visualization of the measure of influence of each of the training points on the prediction.

13 more elements

Dependencies are inferred from the claim text.

patentgrep

Loading patent…

Abstract

A method comprising:

training, by a processor, a regression machine learning model using training data comprising training data points for a historical time-series;
utilizing, by the processor, the trained regression machine learning model to generate a prediction;
receiving, by a machine learning interpretability module, the training data, the trained model and the prediction;
determining, for each of the training data points of the training data, a measure of influence for each of the training data points, wherein a procedure for determining the measure of influence for each of the training data points comprises:
a) removing, by the machine learning interpretability module, the training data point from the training data to form an amended training data set;
b) retraining, by the machine learning interpretability module, the trained regression machine learning model on the amended training data set;
c) utilizing, by the machine learning interpretability module, the retrained regression machine learning model to generate an amended prediction;
d) determining, by the machine learning interpretability module, a difference between the prediction generated by the trained regression machine learning model and the amended prediction generated by the retrained regression machine learning model;
e) determining, by the machine learning interpretability module, a measure of influence for the removed training data point, based on the difference;
f) return the removed training data point to the amended training data set along with the measure of influence for the removed training data point;
g) remove, by the machine learning interpretability module, another training data point from the training data to form another amended training data set;
and
repeat the procedure a)-g) for determining and returning the measure of influence for each of the training data points in the training data;
and
visually outputting the measure of influence of each of the training data points to a graphical user interface so that a visualization of the training data comprises a visualization of the measure of influence of each of the training points on the prediction.

12 more elements