An example embodiment may involve a machine learning model representing relationships between a dependent variable and a plurality of n independent variables. The dependent variable may be a function of the n independent variables, where the n independent variables are measurable characteristics of computing devices, and where the dependent variable is a predicted behavior of the computing devices. The embodiment may also involve obtaining a target value of the dependent variable, and separating the n independent variables into n−1 independent variables with fixed values and a particular independent variable with an unfixed value. The embodiment may also involve performing a partial inversion of the function to produce a value of the particular independent variable such that, when the function is applied to the value of the particular independent variable and the n−1 independent variables with fixed values, the dependent variable is within a pre-defined range of the target value.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: a machine learning model representing relationships between a dependent variable, a first independent variable, and a second independent variable, wherein the dependent variable is a function of the first independent variable and the second independent variable, wherein the first independent variable and the second independent variable represent measurable characteristics of users of one or more computer-based services offered by a managed network, and wherein the dependent variable is a predicted behavior of the users when interacting with the one or more computer-based services; and a computing device including a processor and memory, wherein execution, by the processor, of program instructions stored in the memory causes the computing device to perform operations comprising: obtaining a target value of the dependent variable, determining a fixed value for the first independent variable, performing a partial inversion of the function to produce one or more values of the second independent variable such that, when the function is applied to the first independent variable having the fixed value and any of the one or more values of the second independent variable, the dependent variable is within a pre-defined range of the target value of the dependent variable, and providing the one or more values of the second independent variable for display or to a software application executing within the system. 2. The system of claim 1, wherein performing the partial inversion of the function comprises: iteratively selecting a plurality of candidate values for the second independent variable; evaluating each respective candidate value of the candidate values to obtain a respective output value, wherein the evaluating involves applying the function to: the respective candidate value, and the first independent variable having the fixed value; and selecting the one or more values of the second independent variable with corresponding respective output values that are within the pre-defined range of the target value of the dependent variable. 3. The system of claim 2, wherein selecting the plurality of candidate values for the second independent variable comprises: determining that applying the function to a particular candidate value and the first independent variable having the fixed value results in a particular output value that is within the pre-defined range of the target value of the dependent variable; and selecting at least one further candidate value to be closer to the particular candidate value than any other of the candidate values. 4. The system of claim 2, wherein evaluating each respective candidate value of the candidate values comprises: determining the respective candidate values in accordance with a binary search over a range of the candidate values. 5. The system of claim 2, wherein selecting the plurality of candidate values for the second independent variable comprises: randomly selecting the plurality of candidate values. 6. The system of claim 1, wherein performing the partial inversion of the function comprises: using a non-linear solver software module to determine the one or more values of the second independent variable. 7. The system of claim 1, wherein the operations further comprise: monitoring the measurable characteristics of the users of the one or more computer-based services corresponding to the second independent variable. 8. The system of claim 7, wherein the operations further comprise: determining that the measurable characteristics of the users of the one or more computer-based services is within a threshold range of at least one of the one or more values of the second independent variable; and generating and sending an alert related to the measurable characteristic. 9. The system of claim 8, wherein generating and sending the alert related to the measurable characteristic comprises: transmitting the alert by way of email, voice call, or text message. 10. The system of claim 1, wherein dependent variable represents a desirable behavior of the users of the one or more computer-based services. 11. The system of claim 10, wherein the first independent variable and the second independent variable represent responses of the users to settings of the one or more computer-based services, and the machine learning model predicts an impact of the settings on the behavior of the users. 12. The system of claim 1, wherein the operations further comprise: performing a second partial inversion of the function to produce one or more values of the first independent variable such that, when the function is applied to any of the one or more values of the first independent variable and the second independent variable with a second fixed value, the dependent variable is within the pre-defined range of the target value of the dependent variable. 13. A method comprising: obtaining, by a computing system, a machine learning model representing relationships between a dependent variable, a first independent variable, and a second independent variable, wherein the dependent variable is a function of the first independent variable and the second independent variable, wherein the first independent variable and the second independent variable represent measurable characteristics of users of one or more computer-based services offered by a managed network, and wherein the dependent variable is a predicted behavior of the users when interacting with the one or more computer-based services; obtaining, by the computing system, a target value of the dependent variable; determining, by the computing system, a fixed value for the first independent variable; performing, by the computing system, a partial inversion of the function to produce one or more values of the second independent variable such that, when the function is applied to the first independent variable having the fixed value and any of the one or more values of the second independent variable, the dependent variable is within a pre-defined range of the target value of the dependent variable; and providing, by the computing system, the one or more values of the second independent variable for display or to a software application executing within the system. 14. The method of claim 13, wherein performing the partial inversion of the function comprises: iteratively selecting a plurality of candidate values for the second independent variable; evaluating each respective candidate value of the candidate values to obtain a respective output value, wherein the evaluating involves applying the function to: the respective candidate value, and the first independent variable having the fixed value; and selecting the one or more values of the second independent variable with corresponding respective output values that are within the pre-defined range of the target value of the dependent variable. 15. The method of claim 14, wherein selecting the plurality of candidate values for the second independent variable comprises: determining that applying the function with a particular candidate value and the first independent variable having the fixed value results in a particular output value that is within the pre-defined range of the target value of the dependent variable; and selecting at least one further candidate value to be closer to the particular candidate value than any other of the candidate values. 16. The method of claim 14, wherein evaluating each respective candidate value of the candidate values comprises: determining the respective candidate values in accordance with a binary search over a range of the candidate values. 17. The method of claim 13, wherein performing the partial inversion of the function comprises: using a non-linear solver software module to determine the one or more values of the second independent variable. 18. The method of claim 13, wherein the operations further comprise: monitoring the measurable characteristics of the users of the one or more computer-based services corresponding to the second independent variable; determining that the measurable characteristics of the users of the one or more computer-based services is within a threshold range of at least one of the one or more values of the second independent variable; and generating and sending an alert related to the measurable characteristic. 19. The method of claim 13, wherein dependent variable represents a desirable behavior of the users of the one or more computer-based services, and wherein the first independent variable and the second independent variable represent responses of the users to settings of the one or more computer-based services, and the machine learning model predicts an impact of the settings on the behavior of the users. 20. An article of manufacture including a non-transitory computer-readable medium, having stored thereon program instructions that, upon execution by a computing system, cause the computing system to perform operations comprising: obtaining a machine learning model representing relationships between a dependent variable, a first independent variable, and a second independent variable, wherein the dependent variable is a function of the first independent variable and the second independent variable, wherein the first independent variable and the second independent variable represents measurable characteristics of users of one or more computer-based services offered by a managed network, and wherein the dependent variable is a predicted behavior of the users when interacting with the one or more computer-based services; obtaining a target value of the dependent variable; determining a fixed value for the first independent variable; performing a partial inversion of the function to produce one or more values of the second independent variable such that, when the function is applied to the first independent variable having the fixed value and any of the one or more values of the second independent variable, the dependent variable is within a pre-defined range of the target value of the dependent variable; and providing the one or more values of the second independent variable for display or to a software application executing within the system.