PREDICTING OUTCOMES OF A MODELED SYSTEM USING DYNAMIC FEATURES ADJUSTMENT
First Claim
1. A computer-implemented method comprising:
- receiving, from a database in electronic communication with a processor, analytical data representing a plurality of website metric variables;
designating one of the variables as an output variable and each of the remaining variables as input variables;
computing, by the processor, first result data representing a quantifiable effect of one of the input variables on the output variable relative to at least one of the other input variables based on the analytical data; and
presenting, via a graphical user interface, the first result data in human readable form.
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Accused Products
Abstract
Techniques are disclosed for predicting outcomes of a system modeled on analytical data related to website-related metrics by dynamically adjusting one or more input or output variables. A regularized singular value decomposition technique can be used to estimate missing data. The completed data set can be used to model the performance of the website and to predict various outcomes by changing one or more of the input or output variables. The effect of varying one or more input variables on an output variable can be computed using regression analysis and/or a Random Forests® framework to estimate the relationships between the variables in the model. The effect of specific changes to one or more input variables on one or more output variables can be computed. The amount of change to an input variable needed to achieve a specific change in an output variable can be computed using regression analysis.
16 Citations
20 Claims
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1. A computer-implemented method comprising:
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receiving, from a database in electronic communication with a processor, analytical data representing a plurality of website metric variables; designating one of the variables as an output variable and each of the remaining variables as input variables; computing, by the processor, first result data representing a quantifiable effect of one of the input variables on the output variable relative to at least one of the other input variables based on the analytical data; and presenting, via a graphical user interface, the first result data in human readable form. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system comprising:
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a storage; a display configured to provide a graphical user interface; and a processor operatively coupled to the storage and the display, the processor configured to execute instructions stored in the storage that when executed cause the processor to carry out a process comprising; receiving, from a database in electronic communication with the processor, analytical data representing a plurality of website metric variables; designating one of the variables as an output variable and each of the remaining variables as input variables; computing first result data representing a quantifiable effect of one of the input variables on the output variable relative to at least one of the other input variables based on the analytical data; and presenting, via the graphical user interface, the first result data in human readable form. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A computer-implemented method comprising:
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receiving, from a database in electronic communication with a processor, analytical data representing a plurality of website metric variables; estimating at least one missing value in the analytical data based on non-missing values in the analytical data using a regularized singular value decomposition; designating one of the variables as an output variable and each of the remaining variables as input variables; evaluating, by the processor, a quantifiable effect of one of the input variables on the output variable relative to at least one of the other input variables based on the analytical data; evaluating, by the processor, a magnitude of change of the output variable caused by a predicted adjustment of the one or more input variables using linear regression; and determining, by the processor, a determinant adjustment of the one or more input variables needed to affect a desired change in the output variable using linear regression. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification