MACHINE-LEARNING TECHNIQUES FOR PREDICTING ACTIONS AND BEHAVIOR
Techniques are disclosed for generating explanatory data for predictions generated by a machine learning model. An explanatory engine receives a trained machine learning model and a plurality of predictions generated by the machine learning model. The explanatory engine calculates a predictive strength for the trained machine learning model based on the plurality of predictions. The explanatory engine further determines one or more features having at least a threshold influence on the plurality of predictions and displays, via a graphical user interface, one or more of the influential features and an indication of the predictive strength of the trained model.
1 . A computer-implemented method for generating explanatory data associated with a machine learning model, the method comprising:
receiving a trained machine learning model and a plurality of predictions generated by the machine learning model;
calculating, based on the plurality of predictions, a predictive strength for the trained machine learning model;
determining, based on the plurality of predictions and a plurality of features included in the trained machine learning model, one or more of the plurality of features having at least a threshold influence on the plurality of predictions; and
displaying, via a graphical user interface, one or more of the plurality of features and an indication of the predictive strength of the trained model.
2 . The computer-implemented method of claim 1 , wherein each of the plurality of predictions includes a predicted probability, the computer-implemented method further comprising:
determining, based on the plurality of predictions and the plurality of features included in the trained machine learning model, one or more of the plurality of features for which an increase in a value of the feature increases the predicted probability included in a prediction of the plurality of predictions; and
displaying, via a graphical user interface, the one or more of the plurality of features.
3 . The computer-implemented method of claim 1 , wherein each of the plurality of predictions includes a predicted probability, the computer-implemented method further comprising:
determining, based on the plurality of predictions and the plurality of features included in the trained machine learning model, one or more of the plurality of features for which an increase in a value of the feature decreases the predicted probability included in a prediction of the plurality of predictions; and
displaying, via a graphical user interface, the one or more of the plurality of features.
4 . The computer-implemented method of claim 1 , further comprising displaying, via the graphical user interface, an indication of a number of features included in the trained machine learning model.
5 . The computer-implemented method of claim 3 , further comprising:
receiving, via the graphical user interface, an indication of a selected subset of the plurality of predictions;
calculating, for the selected subset, a quantity of predictions included in the selected subset; and
determining a comparative relationship between first predicted probabilities associated with the selected subset and second predicted probabilities associated with the plurality of predictions.
6 . The computer-implemented method of claim 1 , wherein calculating the predictive strength for the trained machine learning model further comprises calculating a quantitative lift value for the trained machine learning model.
7 . The computer-implemented method of claim 6 , wherein calculating the predictive strength for the trained machine learning model further comprises:
assigning one of a plurality of qualitative labels to the machine learning model, wherein each of the plurality of qualitative labels is associated with a predetermined range of quantitative lift values.
8 . One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of:
receiving a trained machine learning model and a plurality of predictions generated by the machine learning model;
calculating, based on the plurality of predictions, a predictive strength for the trained machine learning model;
determining, based on the plurality of predictions and a plurality of features included in the trained machine learning model, one or more of the plurality of features having at least a threshold influence on the plurality of predictions; and
displaying, via a graphical user interface, one or more of the plurality of features and an indication of the predictive strength of the trained model.
9 . The one or more non-transitory computer-readable media of claim 8 , wherein each of the plurality of predictions includes a predicted probability and wherein the instructions further cause the one or more processors to perform the steps of:
determining, based on the plurality of predictions and the plurality of features included in the trained machine learning model, one or more of the plurality of features for which an increase in a value of the feature increases the predicted probability included in a prediction of the plurality of predictions; and
displaying, via a graphical user interface, the one or more of the plurality of features.
10 . The one or more non-transitory computer-readable media of claim 8 , wherein each of the plurality of predictions includes a predicted probability and wherein the instructions further cause the one or more processors to perform the steps of:
determining, based on the plurality of predictions and the plurality of features included in the trained machine learning model, one or more of the plurality of features for which an increase in a value of the feature decreases the predicted probability included in a prediction of the plurality of predictions; and
displaying, via a graphical user interface, the one or more of the plurality of features.
11 . The one or more non-transitory computer-readable media of claim 10 , wherein the instructions further cause the one or more processors to perform the steps of:
receiving, via the graphical user interface, an indication of a selected subset of the plurality of predictions;
calculating, for the selected subset, a quantity of predictions included in the selected subset; and
determining a comparative relationship between first predicted probabilities associated with the selected subset and second predicted probabilities associated with the plurality of predictions.
12 . The one or more non-transitory computer-readable media of claim 8 , wherein the instructions further cause the one or more processors to perform the steps of:
displaying, via the graphical user interface, an indication of a number of features included in the trained machine learning model.
13 . The one or more non-transitory computer-readable media of claim 8 , wherein the instructions that cause the one or more processors to calculate the predictive strength for the trained machine learning model further cause the one or more processors to calculate a quantitative lift value for the trained machine learning model.
14 . The one or more non-transitory computer-readable media of claim 13 , wherein the instructions, when calculating the predictive strength for the trained machine learning model, further cause the one or more processors to:
assign one of a plurality of qualitative labels to the machine learning model, wherein each of the plurality of qualitative labels is associated with a predetermined range of quantitative lift values.
15 . A system comprising:
one or more memories storing instructions; and
one or more processors for executing the instructions to:
receive a trained machine learning model and a plurality of predictions generated by the machine learning model;
calculate, based on the plurality of predictions, a predictive strength for the trained machine learning model;
determine, based on the plurality of predictions and a plurality of features included in the trained machine learning model, one or more of the plurality of features having at least a threshold influence on the plurality of predictions; and
display, via a graphical user interface, one or more of the plurality of features and an indication of the predictive strength of the trained model.
16 . The system of claim 15 , wherein each of the plurality of predictions includes a predicted probability and the one or more processors further execute the instructions to:
determine, based on the plurality of predictions and the plurality of features included in the trained machine learning model, one or more of the plurality of features for which an increase in a value of the feature increases the predicted probability included in a prediction of the plurality of predictions; and
display, via a graphical user interface, the one or more of the plurality of features.
17 . The system of claim 15 , wherein each of the plurality of predictions includes a predicted probability and the one or more processors further execute the instructions to:
determine, based on the plurality of predictions and the plurality of features included in the trained machine learning model, one or more of the plurality of features for which an increase in a value of the feature decreases the predicted probability included in a prediction of the plurality of predictions; and
display, via a graphical user interface, the one or more of the plurality of features.
18 . The system of claim 17 , wherein the one or more processors further execute the instructions to:
receive, via the graphical user interface, an indication of a selected subset of the plurality of predictions;
calculate, for the selected subset, a quantity of predictions included in the selected subset; and
determine a comparative relationship between first predicted probabilities associated with the selected subset and second predicted probabilities associated with the plurality of predictions.
19 . The system of claim 15 , wherein the one or more processors further execute the instructions to:
display, via the graphical user interface, an indication of a number of features included in the trained machine learning model.
20 . The system of claim 15 , wherein the one or more processors, when calculating the predictive strength for the trained machine learning model further, are configured to calculate a quantitative lift value for the trained machine learning model.