Machine learning with partial inversion

A method comprising:

• determining, by a computing system, a machine learning model representing relationships between a dependent variable and a plurality of n independent variables, wherein the dependent variable is a function of the n independent variables, wherein the n independent variables are measurable characteristics of one or more computing devices of a remote network management platform, and wherein the dependent variable is a predicted behavior of the one or more computing devices;
• obtaining, by the computing system, a target value of the dependent variable;
• separating, by the computing system, the n independent variables into n−1 independent variables with fixed values and a particular independent variable with an unfixed value;
• performing, by the computing system, a partial inversion of the function to produce one or more values of the particular independent variable such that, when the function is applied to any of the one or more values 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 of the dependent variable, wherein performing the partial inversion of the function comprises:
• iteratively selecting a plurality of candidate values for the particular independent variable;
• evaluating each respective candidate value of the candidate values by applying the function to the respective candidate value and the n−1 independent variables with fixed values to obtain a respective output value;
• and
• selecting the one or more values of the particular independent variable with corresponding respective output values that are within the pre-defined range of the target value of the dependent variable;
• and
• providing, by the computing system, the one or more values of the particular independent variable for display or to a software application executing within the remote network management platform.

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:

• determining a machine learning model representing relationships between a dependent variable and a plurality of n independent variables, wherein the dependent variable is a function of the n independent variables, wherein the n independent variables are measurable characteristics of one or more computing devices of a remote network management platform, and wherein the dependent variable is a predicted behavior of the one or more computing devices;
• obtaining a target value of the dependent variable;
• separating the n independent variables into n−1 independent variables with fixed values and one particular independent variable with an unfixed value;
• performing a partial inversion of the function to produce one or more values of the particular independent variable such that, when the function is applied to any of the one or more values 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 of the dependent variable, wherein performing the partial inversion of the function comprises:
• iteratively selecting a plurality of candidate values for the particular independent variable;
• evaluating each respective candidate value of the candidate values by applying the function to the respective candidate value and the n−1 independent variables with fixed values to obtain a respective output value;
• and
• selecting the one or more values of the particular independent variable with corresponding respective output values that are within the pre-defined range of the target value of the dependent variable;
• and
• providing the one or more values of the particular independent variable for display or to a software application executing within the remote network management platform.