Systems and techniques for predictive data analytics
First Claim
1. A predictive modeling apparatus comprising:
- a memory configured to store a machine-executable module encoding a predictive modeling procedure, wherein the predictive modeling procedure includes a plurality of tasks, wherein the machine-executable module includes a directed graph representing dependencies between the tasks, and wherein the plurality of tasks includes at least one pre-processing task, at least one model-fitting task, and at least one post-processing task; and
at least one processor configured to execute the machine-executable module, wherein executing the machine-executable module causes the apparatus to perform the predictive modeling procedure, including;
manipulating input data, comprising performing the pre-processing task on the input data;
performing the model-fitting task, comprising;
generating, from the pre-processed input data, training data and testing data,fitting a predictive model to the training data, andtesting the fitted model on the testing data; and
performing the post-processing task,wherein the pre-processed input data comprise at least one data set, wherein generating the training data comprises obtaining a first subset of the data set, and wherein generating the testing data comprises obtaining a second subset of the data set,wherein performing the predictive modeling procedure further includes performing cross-validation of the predictive model,wherein the training data are first training data, wherein the testing data are first testing data, wherein the fitted model is a first fitted model, and wherein performing the cross-validation of the predictive model comprises;
(a) generating, from the data set, second training data and second testing data, wherein generating the second training data comprises obtaining a third subset of the data set, and wherein generating the second testing data comprises obtaining a fourth subset of the data set;
(b) fitting the predictive model to the second training data to obtain a second fitted model; and
(c) testing the second fitted model on the second testing data.
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Abstract
Systems and techniques for predictive data analytics are described. In a method for selecting a predictive model for a prediction problem, the suitabilities of predictive modeling procedures for the prediction problem may be determined based on characteristics of the prediction problem and/or on attributes of the respective modeling procedures. A subset of the predictive modeling procedures may be selected based on the determined suitabilities of the selected modeling procedures for the prediction problem. A resource allocation schedule allocating computational resources for execution of the selected modeling procedures may be generated, based on the determined suitabilities of the selected modeling procedures for the prediction problem. Results of the execution of the selected modeling procedures in accordance with the resource allocation schedule may be obtained. A predictive model for the prediction problem may be selected based on those results.
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Citations
90 Claims
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1. A predictive modeling apparatus comprising:
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a memory configured to store a machine-executable module encoding a predictive modeling procedure, wherein the predictive modeling procedure includes a plurality of tasks, wherein the machine-executable module includes a directed graph representing dependencies between the tasks, and wherein the plurality of tasks includes at least one pre-processing task, at least one model-fitting task, and at least one post-processing task; and at least one processor configured to execute the machine-executable module, wherein executing the machine-executable module causes the apparatus to perform the predictive modeling procedure, including; manipulating input data, comprising performing the pre-processing task on the input data; performing the model-fitting task, comprising; generating, from the pre-processed input data, training data and testing data, fitting a predictive model to the training data, and testing the fitted model on the testing data; and performing the post-processing task, wherein the pre-processed input data comprise at least one data set, wherein generating the training data comprises obtaining a first subset of the data set, and wherein generating the testing data comprises obtaining a second subset of the data set, wherein performing the predictive modeling procedure further includes performing cross-validation of the predictive model, wherein the training data are first training data, wherein the testing data are first testing data, wherein the fitted model is a first fitted model, and wherein performing the cross-validation of the predictive model comprises; (a) generating, from the data set, second training data and second testing data, wherein generating the second training data comprises obtaining a third subset of the data set, and wherein generating the second testing data comprises obtaining a fourth subset of the data set; (b) fitting the predictive model to the second training data to obtain a second fitted model; and (c) testing the second fitted model on the second testing data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A predictive modeling apparatus comprising:
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a memory configured to store a machine-executable module encoding a predictive modeling procedure, wherein the predictive modeling procedure includes a plurality of tasks, wherein the machine-executable module includes a directed graph representing dependencies between the tasks, and wherein the plurality of tasks includes at least one pre-processing task, at least one model-fitting task, and at least one post-processing task; and at least one processor configured to execute the machine-executable module, wherein executing the machine-executable module causes the apparatus to perform the predictive modeling procedure, including; manipulating input data, comprising performing the pre-processing task on the input data; performing the model-fitting task, comprising; generating, from the pre-processed input data, training data and testing data, fitting a predictive model to the training data, and testing the fitted model on the testing data; and performing the post-processing task, wherein performing the predictive modeling procedure further includes performing nested cross-validation of the predictive model, and wherein; the pre-processed input data comprise at least one data set; performing the nested cross-validation of the predictive model comprises; partitioning the data set into a first plurality of partitions of the data set including at least a first partition of the data set and a second partition of the data set, and partitioning the first partition of the data set into a plurality of partitions of the first partition of the data set including at least a first partition of the first partition of the data set and a second partition of the first partition of the data set; the training data comprise the first partition of the first partition of the data set; and the testing data comprise all of the partitions of the first partition of the data set except the first partition of the first partition of the data set. - View Dependent Claims (17, 18, 19, 20, 21, 22, 23, 24, 25, 26)
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27. A predictive modeling apparatus comprising:
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a memory configured to store a machine-executable module encoding a predictive modeling procedure, wherein the predictive modeling procedure includes a plurality of tasks, wherein the machine-executable module includes a directed graph representing dependencies between the tasks, and wherein the plurality of tasks includes at least one pre-processing task, at least one model-fitting task, and at least one post-processing task; and at least one processor configured to execute the machine-executable module, wherein executing the machine-executable module causes the apparatus to perform the predictive modeling procedure, including; manipulating input data, comprising performing the pre-processing task on the input data performing the model-fitting task, comprising; generating, from the pre-processed input data, training data and testing data, fitting a predictive model to the training data, and testing the fitted model on the testing data; and performing the post-processing task, wherein the pre-processed input data comprise at least one data set, wherein generating the training data comprises obtaining a first subset of the data set, and wherein generating the testing data comprises obtaining a second subset of the data set, wherein the predictive model is a first type of predictive model, the fitted model is a first fitted model, the model-fitting task is a first model-fitting task, and performing the predictive modeling procedure further includes performing a second model-fitting task using a second type of predictive model. - View Dependent Claims (28, 29, 30, 31, 32, 33, 34, 35, 36, 37)
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38. A predictive modeling apparatus comprising:
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a memory configured to store a machine-executable module encoding a predictive modeling procedure, wherein the predictive modeling procedure includes a plurality of tasks, wherein the machine-executable module includes a directed graph representing dependencies between the tasks, and wherein the plurality of tasks includes at least one pre-processing task, at least one model-fitting task, and at least one post-processing task; and at least one processor configured to execute the machine-executable module, wherein executing the machine-executable module causes the apparatus to perform the predictive modeling procedure, including; manipulating input data, comprising performing the pre-processing task on the input data; performing the model-fitting task, comprising; generating, from the pre-processed input data, training data and testing data, fitting a predictive model to the training data, and testing the fitted model on the testing data; and performing the post-processing task, wherein the at least one processor is further configured to deploy the fitted model, wherein the fitted model has a first representation, and wherein deploying the fitted model comprises;
generating a second representation of the fitted model, wherein the second representation comprises a set of one or more conditional rules,wherein the second representation of the fitted model is a machine executable representation, and wherein the set of one or more conditional rules comprises a set of one or more machine executable if-then statements. - View Dependent Claims (39, 40)
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41. A predictive modeling apparatus comprising:
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a memory configured to store a machine-executable module encoding a predictive modeling procedure, wherein the predictive modeling procedure includes a plurality of tasks, wherein the machine-executable module includes a directed graph representing dependencies between the tasks, and wherein the plurality of tasks includes at least one pre-processing task, at least one model-fitting task, and at least one post-processing task; and at least one processor configured to execute the machine-executable module, wherein executing the machine-executable module causes the apparatus to perform the predictive modeling procedure, including; manipulating input data, comprising performing the pre-processing task on the input data; performing the model-fitting task, comprising; generating, from the pre-processed input data, training data and testing data, fitting a predictive model to the training data, and testing the fitted model on the testing data; and performing the post-processing task, wherein the at least one processor is further configured to deploy the fitted model, wherein the input data are first input data, and wherein deploying the fitted model further comprises refreshing the fitted model based, at least in part, on second input data. - View Dependent Claims (42, 43, 44, 45, 46, 47)
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48. A predictive modeling method comprising:
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accessing a machine-executable module encoding a predictive modeling procedure, wherein the predictive modeling procedure includes a plurality of tasks, wherein the machine-executable module includes a directed graph representing dependencies between the tasks, and wherein the plurality of tasks includes at least one pre-processing task, at least one model-fitting task, and at least one post-processing task; and executing the machine-executable module, wherein executing the machine-executable module comprises performing the predictive modeling procedure, including; manipulating input data, comprising performing the pre-processing task on the input data; performing the model-fitting task, comprising; generating, from the pre-processed input data, training data and testing data, fitting a predictive model to the training data, and testing the fitted model on the testing data; and performing the post-processing task, wherein the pre-processed input data comprise at least one data set, wherein generating the training data comprises obtaining a first subset of the data set, and wherein generating the testing data comprises obtaining a second subset of the data set, wherein performing the predictive modeling procedure further includes performing cross-validation of the predictive model, wherein the training data are first training data, wherein the testing data are first testing data, wherein the fitted model is a first fitted model, and wherein performing the cross-validation of the predictive model comprises; (a) generating, from the data set, second training data and second testing data, wherein generating the second training data comprises obtaining a third subset of the data set, and wherein generating the second testing data comprises obtaining a fourth subset of the data set; (b) fitting the predictive model to the second training data to obtain a second fitted model; and (c) testing the second fitted model on the second testing data. - View Dependent Claims (49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62)
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63. A predictive modeling method comprising:
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accessing a machine-executable module encoding a predictive modeling procedure, wherein the predictive modeling procedure includes a plurality of tasks, wherein the machine-executable module includes a directed graph representing dependencies between the tasks, and wherein the plurality of tasks includes at least one pre-processing task, at least one model-fitting task, and at least one post-processing task; and executing the machine-executable module, wherein executing the machine-executable module comprises performing the predictive modeling procedure, including; manipulating input data, comprising performing the pre-processing task on the input data; performing the model-fitting task, comprising; generating, from the pre-processed input data, training data and testing data, fitting a predictive model to the training data, and testing the fitted model on the testing data; and performing the post-processing task, wherein performing the predictive modeling procedure further includes performing nested cross-validation of the predictive model, and wherein; the pre-processed input data comprise at least one data set; performing the nested cross-validation of the predictive model comprises; partitioning the data set into a first plurality of partitions of the data set including at least a first partition of the data set and a second partition of the data set, and partitioning the first partition of the data set into a plurality of partitions of the first partition of the data set including at least a first partition of the first partition of the data set and a second partition of the first partition of the data set; the training data comprise the first partition of the first partition of the data set; and the testing data comprise all of the partitions of the first partition of the data set except the first partition of the first partition of the data set. - View Dependent Claims (64, 65, 66, 67, 68, 69, 70, 71, 72, 73)
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74. A predictive modeling method comprising:
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accessing a machine-executable module encoding a predictive modeling procedure, wherein the predictive modeling procedure includes a plurality of tasks, wherein the machine-executable module includes a directed graph representing dependencies between the tasks, and wherein the plurality of tasks includes at least one pre-processing task, at least one model-fitting task, and at least one post-processing task; and executing the machine-executable module, wherein executing the machine-executable module comprises performing the predictive modeling procedure, including; manipulating input data, comprising performing the pre-processing task on the input data; performing the model-fitting task, comprising; generating, from the pre-processed input data, training data and testing data, fitting a predictive model to the training data, and testing the fitted model on the testing data; and performing the post-processing task, wherein the pre-processed input data comprise at least one data set, wherein generating the training data comprises obtaining a first subset of the data set, and wherein generating the testing data comprises obtaining a second subset of the data set, wherein the predictive model is a first type of predictive model, the fitted model is a first fitted model, the model-fitting task is a first model-fitting task, and performing the predictive modeling procedure further includes performing a second model-fitting task using a second type of predictive model. - View Dependent Claims (75, 76, 77, 78, 79, 80, 81, 82, 83, 84)
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85. A predictive modeling method comprising:
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accessing a machine-executable module encoding a predictive modeling procedure, wherein the predictive modeling procedure includes a plurality of tasks, wherein the machine-executable module includes a directed graph representing dependencies between the tasks, and wherein the plurality of tasks includes at least one pre-processing task, at least one model-fitting task, and at least one post-processing task; executing the machine-executable module, wherein executing the machine-executable module comprises performing the predictive modeling procedure, including; manipulating input data, comprising performing the pre-processing task on the input data; performing the model-fitting task, comprising; generating, from the pre-processed input data, training data and testing data, fitting a predictive model to the training data, and testing the fitted model on the testing data; and performing the post-processing task; and deploying the fitted model, wherein the fitted model has a first representation, and wherein deploying the fitted model comprises;
generating a second representation of the fitted model, wherein the second representation comprises a set of one or more conditional rules, andwherein the second representation of the fitted model is a machine executable representation, and wherein the set of one or more conditional rules comprises a set of one or more machine executable if-then statements. - View Dependent Claims (86, 87)
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88. A predictive modeling method comprising:
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accessing a machine-executable module encoding a predictive modeling procedure, wherein the predictive modeling procedure includes a plurality of tasks, wherein the machine-executable module includes a directed graph representing dependencies between the tasks, and wherein the plurality of tasks includes at least one pre-processing task, at least one model-fitting task, and at least one post-processing task; executing the machine-executable module, wherein executing the machine-executable module comprises performing the predictive modeling procedure, including; manipulating input data, comprising performing the pre-processing task on the input data; performing the model-fitting task, comprising; generating, from the pre-processed input data, training data and testing data, fitting a predictive model to the training data, and testing the fitted model on the testing data; performing the post-processing task; and deploying the fitted model, wherein the input data are first input data, and wherein deploying the fitted model further comprises refreshing the fitted model based, at least in part, on second input data. - View Dependent Claims (89, 90)
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Specification