A method for determining the predictive value of a feature may include: (a) performing predictive modeling procedures associated with respective predictive models, wherein performing each modeling procedure includes fitting the associated model to an initial dataset representing an initial prediction problem; (b) determining a first accuracy score of each of the fitted models, representing an accuracy with which the fitted model predicts an outcome of the initial prediction problem; (c) shuffling values of a feature across observations included in the initial dataset, thereby generating a modified dataset representing a modified prediction problem; (d) determining a second accuracy score of each of the fitted models, representing an accuracy with which the fitted model predicts an outcome of the modified prediction problem; and (e) determining a model-specific predictive value of the feature for each of the fitted models based on the first and second accuracy scores of the fitted model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented predictive modeling method comprising: (a) performing a plurality of predictive modeling procedures, wherein each of the predictive modeling procedures is associated with a predictive model, wherein performing each modeling procedure comprises fitting the associated predictive model to an initial dataset representing an initial prediction problem, and wherein the initial dataset includes a plurality of observations and each observation includes respective values for a plurality of features; (b) determining a first respective accuracy score of each of the fitted predictive models, wherein the first accuracy score of each fitted model represents an accuracy with which the fitted model predicts one or more outcomes of the initial prediction problem; (c) shuffling values of a feature across respective observations included in the initial dataset, thereby generating a modified dataset representing a modified prediction problem; (d) determining a second respective accuracy score of each of the fitted predictive models, wherein the second accuracy score of each fitted model represents an accuracy with which the fitted model predicts one or more outcomes of the modified prediction problem; (e) determining a respective model-specific predictive value of the feature for each of the fitted models, wherein the model-specific predictive value of the feature for each fitted model is based on the first and second accuracy scores of the fitted model; and (f) based at least in part on the model-specific predictive values of the feature, performing at least one action selected from the group consisting of: pruning a feature from the initial dataset, creating a derived feature from two or more features in the initial dataset and adding the derived feature to the initial dataset, selecting two or more of the fitted predictive models for blending and blending the selected models, and/or allocating resources during a process of evaluating suitabilities of predictive modeling procedures for the initial prediction problem. 2. The method of claim 1, further comprising: prior to performing the plurality of predictive modeling procedures, selecting the plurality of predictive modeling procedures for the prediction problem based on characteristics of the initial dataset, characteristics of the initial prediction problem, and/or characteristics of the feature. 3. The method of claim 1, wherein the plurality of predictive modeling procedures comprises two or more modeling procedures selected from the group consisting of a random forest modeling procedure, a generalized additive modeling procedure, and a support vector machine modeling procedure. 4. The method of claim 1, wherein the plurality of predictive modeling procedures comprises a first modeling procedure selected from a first family of modeling procedures and a second modeling procedure selected from a second family of modeling procedures. 5. The method of claim 1, further comprising: prior to determining the second accuracy scores of the predictive models, refitting the predictive models to the modified dataset representing the modified prediction problem. 6. The method of claim 1, wherein the determined model-specific predictive value of the feature for a particular fitted model increases as the difference between the first accuracy score and the second accuracy score of the particular fitted model increases. 7. The method of claim 1, wherein the determined model-specific predictive value of the feature for a particular fitted model comprises a percentage difference between the first accuracy score and the second accuracy score of the particular fitted model, relative to the first accuracy score of the particular fitted model. 8. The method of claim 1, further comprising determining a model-independent predictive value of the feature based on the model-specific predictive values of the feature. 9. The method of claim 8, wherein determining the model-independent predictive value of the feature comprises calculating a statistical measure of a center and/or a spread of the model-specific predictive values of the feature. 10. The method of claim 8, wherein determining the model-independent predictive value of the feature comprises calculating the statistical measure of the center of model-specific predictive values, and wherein the statistical measure of the center is selected from the group consisting of a mean, a median, and a mode of the model-specific predictive values. 11. The method of claim 8, wherein determining the model-independent predictive value of the feature comprises calculating the statistical measure of the spread of model-specific predictive values, and wherein the statistical measure of the spread is selected from the group consisting of a range, a variance, and a standard deviation of the model-specific predictive values. 12. The method of claim 8, wherein determining the model-independent predictive value of the feature comprises calculating a combination of the model-specific predictive values of the feature. 13. The method of claim 12, wherein calculating a combination of the model-specific predictive values comprises calculating a weighted combination of the model-specific predictive values. 14. The method of claim 13, wherein calculating the weighted combination of the model-specific predictive values comprises assigning respective weights to the model-specific predictive values, wherein the weight assigned to a particular model-specific predictive value corresponding to a particular fitted predictive model increases as the first accuracy score of the fitted predictive model increases. 15. The method of claim 1, wherein the feature is a first feature, and wherein the method further comprises: (c1) shuffling values of a second feature across the respective observations included in the initial dataset, thereby generating a second modified dataset representing a second modified prediction problem; (d1) determining a third respective accuracy score of each of the fitted predictive models, wherein the third accuracy score of each fitted model represents an accuracy with which the fitted model predicts one or more outcomes of the second modified prediction problem; and (e1) determining a respective model-specific predictive value of the second feature for each of the fitted models, wherein the model-specific predictive value of the second feature for each fitted model is based on the first and third accuracy scores of the fitted model. 16. The method of claim 1, wherein the feature is a first feature, wherein the initial dataset includes the first feature and a plurality of second features, and wherein the method further comprises determining model-specific predictive values of the second features of the initial dataset by performing steps (c), (d), and (e) for each of the second features. 17. The method of claim 16, further comprising displaying, via a graphical user interface, graphical content identifying the first and second features of the initial dataset and the model-specific predictive values of the first and second features. 18. The method of claim 16, wherein the modeling procedures are first modeling procedures including a particular modeling procedure associated with a particular predictive model, wherein the model-specific predictive values of the first and second features include particular model-specific predictive values of the first and second features that are specific to the particular predictive model, and wherein the method further comprises (a1) performing a plurality of second predictive modeling procedures including the particular modeling procedure associated with the particular predictive model. 19. The method of claim 18, wherein performing the particular predictive modeling procedures comprises performing feature engineering on the initial dataset based on the particular model-specific predictive values of the first feature and the second features. 20. The method of claim 19, wherein performing feature engineering comprises removing a particular feature from the initial dataset based on the particular feature having a low model-specific predictive value. 21. The method of claim 20, further comprising determining that the model-specific predictive value of the particular feature is low based on the model-specific predictive value of the particular feature being lower than a threshold value and/or based on the model-specific predictive value of the particular feature being in a specified percentile of the particular model-specific predictive values for the first and second features of the initial dataset. 22. The method of claim 19, wherein performing feature engineering comprises: generating a derived feature based on two or more particular features of the initial dataset having high model-specific predictive values; and adding the derived feature to the initial dataset, thereby generating a second initial dataset. 23. The method of claim 22, further comprising determining that the model-specific predictive values of the particular features are high based on the model-specific predictive values of the particular features being higher than a threshold value and/or based on the model-specific predictive values of the particular features being in a specified percentile of the particular model-specific predictive values for the first and second features of the initial dataset. 24. The method of claim 22, wherein performing the particular predictive modeling procedure further comprises fitting the particular predictive model to the second initial dataset, and wherein the method further comprises: determining a first accuracy score of the fitted particular predictive model, wherein the first accuracy score of the fitted particular model represents an accuracy with which the fitted particular model predicts one or more outcomes of the initial prediction problem; shuffling values of the first feature across respective observations included in the second initial dataset, thereby generating a second modified dataset representing a second modified prediction problem; determining a second accuracy score of the fitted particular predictive model, wherein the second accuracy score of the fitted particular model represents an accuracy with which the fitted model predicts one or more outcomes of the second modified prediction problem; and determining a second model-specific predictive value of the first feature for the fitted particular model, wherein the second model-specific predictive value of the first feature for the fitted particular model is based on the first and second accuracy scores of the fitted particular model. 25. The method of claim 18, further comprising, prior to performing the plurality of second modeling procedures: selecting the second modeling procedures based on suitabilities of the selected modeling procedures for the initial prediction problem, wherein a suitability of the particular predictive modeling procedure for the initial prediction problem is determined based, at least in part, on characteristics of one or more particular features of the initial dataset having high model-specific predictive values for the particular predictive modeling procedure. 26. The method of claim 25, further comprising: transmitting instructions to a plurality of processing nodes, the instructions comprising a resource allocation schedule allocating resources of the processing nodes for execution of the second modeling procedures, the resource allocation schedule being based, at least in part, on the suitabilities of the second modeling procedures for the initial prediction problem; receiving results of the execution of the second modeling procedures by the plurality of processing nodes in accordance with the resource allocation schedule, wherein the results include predictive models generated by the second modeling procedures, and/or scores of the generated models for data associated with the initial prediction problem; and selecting, from the generated models, a predictive model for the initial prediction problem based, at least in part, on the score of the selected predictive model. 27. The method of claim 25, further comprising: generating a blended predictive model by combining two or more of the generated predictive models; and evaluating the blended predictive model. 28. The method of claim 1, wherein a predictive value of the feature for the modified prediction problem is less than a threshold predictive value. 29. A predictive modeling apparatus comprising: a memory configured to store processor-executable instructions; and a processor configured to execute the processor-executable instructions, wherein executing the processor-executable instructions causes the apparatus to perform steps including: (a) performing a plurality of predictive modeling procedures, wherein each of the predictive modeling procedures is associated with a predictive model, wherein performing each modeling procedure comprises fitting the associated predictive model to an initial dataset representing an initial prediction problem, and wherein the initial dataset includes a plurality of observations and each observation includes respective values for a plurality of features; (b) determining a first respective accuracy score of each of the fitted predictive models, wherein the first accuracy score of each fitted model represents an accuracy with which the fitted model predicts one or more outcomes of the initial prediction problem; (c) shuffling values of a feature across respective observations included in the initial dataset, thereby generating a modified dataset representing a modified prediction problem; (d) determining a second respective accuracy score of each of the fitted predictive models, wherein the second accuracy score of each fitted model represents an accuracy with which the fitted model predicts one or more outcomes of the modified prediction problem; and (e) determining a respective model-specific predictive value of the feature for each of the fitted models, wherein the model-specific predictive value of the feature for each fitted model is based on the first and second accuracy scores of the fitted model; and (f) based at least in part on the model-specific predictive values of the feature, performing at least one action selected from the group consisting of: pruning a feature from the initial dataset, creating a derived feature from two or more features in the initial dataset and adding the derived feature to the initial dataset, selecting two or more of the fitted predictive models for blending and blending the selected models, and/or allocating resources during a process of evaluating suitabilities of predictive modeling procedures for the initial prediction problem. 30. An article of manufacture having computer-readable instructions stored thereon that, when executed by a processor, cause the processor to perform operations including: (a) performing a plurality of predictive modeling procedures, wherein each of the predictive modeling procedures is associated with a predictive model, wherein performing each modeling procedure comprises fitting the associated predictive model to an initial dataset representing an initial prediction problem, and wherein the initial dataset includes a plurality of observations and each observation includes respective values for a plurality of features; (b) determining a first respective accuracy score of each of the fitted predictive models, wherein the first accuracy score of each fitted model represents an accuracy with which the fitted model predicts one or more outcomes of the initial prediction problem; (c) shuffling values of a feature across respective observations included in the initial dataset, thereby generating a modified dataset representing a modified prediction problem; (d) determining a second respective accuracy score of each of the fitted predictive models, wherein the second accuracy score of each fitted model represents an accuracy with which the fitted model predicts one or more outcomes of the modified prediction problem; and (e) determining a respective model-specific predictive value of the feature for each of the fitted models, wherein the model-specific predictive value of the feature for each fitted model is based on the first and second accuracy scores of the fitted model; and (f) based at least in part on the model-specific predictive values of the feature, performing at least one action selected from the group consisting of: pruning a feature from the initial dataset, creating a derived feature from two or more features in the initial dataset and adding the derived feature to the initial dataset, selecting two or more of the fitted predictive models for blending and blending the selected models, and/or allocating resources during a process of evaluating suitabilities of predictive modeling procedures for the initial prediction problem.