Embodiments of the present invention relate to transforming a nominal feature to a numeric feature that indicates a likelihood or probability of a particular outcome. Numeric features are determined that indicate a likelihood of an outcome given the value of the collected data (nominal values). Such numeric features are used to represent the corresponding nominal features for use in generating a machine learned model. As such, a nominal feature initially captured in a data set is transformed or converted to a numeric feature that represents a likelihood of a corresponding outcome as opposed to a Boolean value. Upon transforming nominal values to numeric values based on the likelihood of outcome, the numeric values can be used to generate a machine learned model that is used to predict future outcomes.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. One or more computer storage media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform operations comprising: referencing a nominal feature captured in a data set, wherein the nominal feature comprises a non-numeric value; determining a numeric feature to represent the nominal feature, wherein the numeric feature comprises a likelihood of a particular outcome occurring in accordance with the nominal feature; and using the numeric feature to generate a machine learned model to be used to predict an expected outcome associated with a future observation instance. 2. The one or more computer storage media of claim 1 further comprising identifying the nominal feature within the data set including a plurality of observations. 3. The one or more computer storage media of claim 1, wherein the likelihood of the outcome is calculated by dividing a number of observations including the nominal feature that resulted in a target variable by a total number of observations including the nominal feature. 4. The one or more computer storage media of claim 1, wherein the numeric feature is used to derive a coefficient associated with the numeric feature for the machine learned model. 5. The one or more computer storage media of claim 1, wherein the machine learned model is used to predict the outcome associated with the future observation instance in real-time. 6. The one or more computer storage media of claim 1 further comprising: determining a second numeric feature to represent a second nominal feature, wherein the second numeric feature comprises a likelihood of an outcome in accordance with the second nominal feature; and using the second numeric feature to generate the machine learned model. 7. The one or more computer storage media of claim 1 further comprising replacing the nominal feature with the numeric feature. 8. The one or more computer storage media of claim 1, wherein the machine learned model is used to predict an outcome associated with a website analysis. 9. A computerized method comprising: identifying one or more nominal features within a data set to be transformed to numeric features, each of the one or more nominal features comprising a non-numeric value; for each of the one or more nominal features, automatically generating a probability of a particular outcome occurring given the corresponding nominal feature using previously captured data; transforming the one or more nominal features to one or more numeric features based on the generated probabilities of the particular outcome occurring; and utilizing the one or more numeric features to generate a machine learned model to be used to predict an expected outcome associated with a future observation instance via a computing device. 10. The method of claim 9, wherein the data set includes data associated with a plurality of observations. 11. The method of claim 9, wherein the machine learned model is used to predict outcomes associated with web analytics. 12. The method of claim 9, wherein the machine learned model is used to predict outcomes associated with marketing analytics. 13. The method of claim 9, wherein the one or more numeric features are used to generate the machine learned model by deriving coefficients associated with each of the numeric features. 14. One or more computer storage media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform operations comprising: obtaining data associated with an observation for which a predicted outcome is desired, the data including a nominal feature that has a non-numeric value; referencing a numeric feature that corresponds with the nominal feature, the numeric feature comprising a likelihood of a predetermined outcome based on the value of the nominal feature; and inserting the numeric feature within a machine learned model to predict an outcome for the observation, wherein the machine learned model is generated using a set of captured data having nominal values converted to corresponding numeric values. 15. The one or more computer storage media of claim 14, wherein the nominal feature is an alphabetical text value. 16. The one or more computer storage media of claim 14, wherein the numeric feature that corresponds with the nominal feature is looked up in a lookup system. 17. The one or more computer storage media of claim 14, wherein the numeric feature that corresponds with the nominal feature is generated in real-time using a set of prior observations. 18. The one or more computer storage media of claim 14, wherein the machine learned model is used to predict outcomes associated with web analytics. 19. The one or more computer storage media of claim 14, wherein the machine learned model is used to predict outcomes associated with marketing analytics. 20. The one or more computer storage media of claim 14 further comprising: obtaining data associated with a second observation for which a predicted outcome is desired, the data including the nominal feature; referencing an updated numeric feature that corresponds with the nominal feature, the updated numeric feature comprising a likelihood of a predetermined outcome in accordance with the nominal feature based on more recently captured data; and inserting the updated numeric feature within the machine learned model to predict an outcome for the second observation.