GENERATING SCENARIOS BY MODIFYING VALUES OF MACHINE LEARNING FEATURES
A system to generate scenarios by modifying values of machine learning features is provided. The system can present a first indication in a first coordinate space of a first performance generated by a model trained with a plurality of features using machine learning. The system can present a second indication in a second coordinate space of a first performance of the first feature. The system can receive a modification to a value in the second coordinate space of the first feature. The system can determine a second performance of the model using machine learning based on a first derived feature to output derived data points in the time period. The system can present in the first coordinate space, a third indication of the second performance of the model overlaid with the first indication of the first performance of the model.
1 . A system, comprising:
a data processing system comprising one or more processors, coupled to memory, to:
present, via a user interface, a first indication in a first coordinate space of a first performance generated by a model trained with a plurality of features using machine learning to output a plurality of data points having corresponding time stamps within a time period;
receive, via the user interface, a selection of a first feature of the plurality of features;
present, via the user interface, a second indication in a second coordinate space of a first performance of the first feature, the second indication having corresponding time stamps within the time period;
receive, via the user interface, a modification to a value in the second coordinate space of the first feature of the plurality of features;
generate, responsive to the modification in the second coordinate space, a first derived feature based on the modified value of the first feature;
determine a second performance of the model using machine learning based on the first derived feature to output derived data points having corresponding time stamps within the time period; and
present, via the user interface in the first coordinate space, a third indication of the second performance of the model overlaid with the first indication of the first performance of the model.
2 . The system of claim 1 , the data processing system further configured to:
determine whether one or more of the features are editable; and
present, in response to a determination that the features are editable, via the user interface, a control affordance corresponding to the features,
wherein the selection is received in response to user input at the control affordance.
3 . The system of claim 1 , wherein the control affordance comprises a menu including an item identifying the first feature.
4 . The system of claim 1 , the data processing system further configured to:
present, via the user interface, a first region in the second coordinate space, the first region bounded by a first time stamp in the time period and a second time stamp later than the first time stamp in the time period.
5 . The system of claim 4 , wherein the first region restricts editing of the data points of the first feature to data points having corresponding time stamps in the first region.
6 . The system of claim 1 , the data processing system further configured to:
present, via the user interface, a fourth indication within a third coordinate space, the fourth indication corresponding to a first performance of a second feature and including one or more data points having corresponding time stamps in the time period.
7 . The system of claim 6 , the data processing system further configured to:
generate the third indication with input including the first derived feature and a second derived feature to output the derived points, the second derived feature corresponding to a second performance of the second feature and including one or more data points having corresponding time stamps in the time period.
8 . The system of claim 7 , the data processing system further configured to:
receive, via the user interface, a selection of the second feature among the features;
receive, via the user interface, a modification to a second value in the third coordinate space of the second feature; and
generate, responsive to the modification in the third coordinate space, the second derived feature based on the modified value of the second feature.
9 . The system of claim 8 , the data processing system further configured to:
present, via the user interface, the second coordinate space and the fourth coordinate space concurrently within a graphical user interface presentation.
10 . A method, comprising:
presenting, via a user interface, a first indication in a first coordinate space of a first performance generated by a model trained with a plurality of features using machine learning to output a plurality of data points having corresponding time stamps within a time period;
receiving, via the user interface, a selection of a first feature of the plurality of features;
presenting, via the user interface, a second indication in a second coordinate space of a first performance of the first feature, the second indication having corresponding time stamps within the time period;
receiving, via the user interface, a modification to a value in the second coordinate space of the first feature of the plurality of features;
generating, responsive to the modification in the second coordinate space, a first derived feature based on the modified value of the first feature;
determining a second performance of the model using machine learning based on the first derived feature to output derived data points having corresponding time stamps within the time period; and
presenting, via the user interface in the first coordinate space, a third indication of the second performance of the model overlaid with the first indication of the first performance of the model.
11 . The method of claim 10 , further comprising:
determining whether one or more of the features are editable; and
presenting, in response to a determination that the features are editable, via the user interface, a control affordance corresponding to the features,
wherein the selection is received in response to user input at the control affordance.
12 . The method of claim 10 , wherein the control affordance comprises a menu including an item identifying the first feature.
13 . The method of claim 10 , further comprising:
presenting, via the user interface, a first region in the second coordinate space, the first region bounded by a first time stamp in the time period and a second time stamp later than the first time stamp in the time period.
14 . The method of claim 13 , wherein the first region restricts editing of the data points of the first feature to data points having corresponding time stamps in the first region.
15 . The method of claim 10 , further comprising:
presenting, via the user interface, a fourth indication within a third coordinate space, the fourth indication corresponding to a first performance of a second feature and including one or more data points having corresponding time stamps in the time period.
16 . The method of claim 15 , further comprising:
generating the third indication with input including the first derived feature and a second derived feature to output the derived points, the second derived feature corresponding to a second performance of the second feature and including one or more data points having corresponding time stamps in the time period.
17 . The method of claim 16 , further comprising:
receiving, via the user interface, a selection of the second feature among the features;
receiving, via the user interface, a modification to a second value in the third coordinate space of the second feature; and
generating, responsive to the modification in the third coordinate space, the second derived feature based on the modified value of the second feature.
18 . The method of claim 17 , further comprising:
presenting, via the user interface, the second coordinate space and the fourth coordinate space concurrently within a graphical user interface presentation.
19 . A computer readable medium including one or more instructions stored thereon and executable by a processor to:
present, by the processor and via a user interface, a first indication in a first coordinate space of a first performance generated by a model trained with a plurality of features using machine learning to output a plurality of data points having corresponding time stamps within a time period;
receive, by the processor and via the user interface, a selection of a first feature of the plurality of features;
present, by the processor and via the user interface, a second indication in a second coordinate space of a first performance of the first feature, the second indication having corresponding time stamps within the time period;
receive, by the processor and via the user interface, a modification to a value in the second coordinate space of the first feature of the plurality of features;
generate, by the processor and responsive to the modification in the second coordinate space, a first derived feature based on the modified value of the first feature;
determine, by the processor, a second performance of the model using machine learning based on the first derived feature to output derived data points having corresponding time stamps within the time period; and
present, by the processor and via the user interface in the first coordinate space, a third indication of the second performance of the model overlaid with the first indication of the first performance of the model.
20 . The computer readable medium of claim 19 , wherein the computer readable medium further includes one or more instructions executable by the processor to:
present, via the user interface, a fourth indication within a third coordinate space, the fourth indication corresponding to a first performance of a second feature and including one or more data points having corresponding time stamps in the time period.