There is provided a method for generating features for use in an automated machine learning process, comprising: receiving a first training dataset comprising unclassified raw data instances each including a set of objects of arbitrary types; applying a function to each data instance to calculate a set of first results; generating a set of classification features each including the function for application to a newly received data instance to calculate a second result, and a condition defined by a respective member of the set of first results applied to the second result; applying each classification feature to each instance of an unclassified second training dataset to generate a set of extracted features; selecting a subset of pivotal classification features from the set of classification features according to a correlation requirement between classification variable(s) and each respective member of the set of extracted features; and documenting the subset of pivotal features.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for generating features for use in an automated machine learning process, comprising: receiving a designation of a first training dataset comprising a plurality of raw data instances, each instance including a set of objects of arbitrary types, each object assigned at least one value, the raw data instances being unclassified; applying a function to each data instance to calculate a set of first results, the function being adapted to process at least one object of the set of objects of each instance, the function being adapted to process objects of arbitrary type; generating a set of classification features for application to a newly received data instance, each classification feature including the function for application to the newly received data instance to calculate a second result, and at least one condition defined by at least one respective member of the set of first results applied to the second result; applying each feature of the generated set of classification features to each instance of a second training dataset of unclassified raw data to generate a set of extracted features; selecting a subset of pivotal classification features from the set of classification features according to a correlation requirement between at least one classification variable and each respective member of the set of extracted features; and documenting the selected subset of pivotal features for use in an automated machine learning process. 2. The method of claim 1, further comprising training a statistical classifier for classification of the at least one classification variable based on the selected subset of pivotal features applied to a third training dataset. 3. The method of claim 2, further comprising classifying a new data instance to the at least one classification variable by applying each classification feature of the selected subset of pivotal features to the new data instance to extract a plurality of features and applying the statistical classifier to the plurality of extracted features. 4. The method of claim 1, wherein applying the function comprises applying each function of a set of functions to each data instance to generate a plurality of sets of first results, wherein each respective function is adapted to process different objects. 5. The method of claim 1, wherein the at least one condition comprises equality with the respective member of the set of first results. 6. The method of claim 1, wherein the at least one condition is selected to generate a classification feature of a binary type by selecting a function that implements a binary condition. 7. The method of claim 1, wherein the at least one condition comprises greater-than or less-than the respective member or a value calculated from the respective member of the set of first results. 8. The method of claim 1, wherein each member of the set of first results includes a set of values, and the at least one condition comprises membership in at least one respective set. 9. The method of claim 8, wherein the at least one condition further comprises that the membership in each respective set is greater-than or less-than at least one other respective member of the set of first results. 10. The method of claim 1, wherein at least one member of the set of first results includes a set of values, and the at least one condition comprises an aggregation function applied to at least one respective set. 11. The method of claim 1, wherein at least one member of the set of first results includes a set of key-value pairs, and the at least one condition comprises equality with the respective key of a certain pair and a member selected from the group consisting of: greater than, less than, and equal to, the value of the certain pair. 12. The method of claim 11, wherein the at least one condition is applied to each member of the set of key-value pairs to generate a second set of sub-results, and further comprising applying another at least one aggregation function to the second set of sub-results. 13. The method of claim 1, wherein the arbitrary types are members selected from a set consisting of: time series, vector, map, graph, and tree. 14. The method of claim 1, wherein each instance includes at least two objects having a similar data structure each including a plurality of parameters storing similar representations of similar data, and further comprising creating, for each common value of each parameter of the similar data structure, a set of values of the other parameters of the similar data structure, and wherein applying the function to each data instance to generate a set of first results comprises applying the function to the set of values of the other parameters of the similar data structure. 15. The method of claim 1, wherein the first training dataset and the second training dataset are the same or overlap. 16. A system for generating features for use in an automated machine learning process, comprising: a data interface for communicating with a storage unit storing thereon at least one training dataset comprising a plurality of raw unclassified data instances, each instance including a set of objects of arbitrary types, each object assigned a value, and storing a set of functions adapted to process to each data instance to generate a plurality of sets of first results, each function adapted to process objects of arbitrary type; a program store storing code; and a processor coupled to the data interface and the program store for implementing the stored code, the code comprising: code to apply at least one function from the set of functions to each data instance to calculate a set of first results, the function being adapted to process at least one object of the set of objects of each instance; code responsive to the set of first results, to generate a set of classification features for application to a newly received data instance, each feature including a calculated second result of the function applied to the newly received data instance and an application of at least one condition defined by each member of the set of first results to the second result; and code responsive to the generated set of classification features to apply each classification feature of the generated set of classification features to each instance of a second training set of raw unclassified data instances stored on the storage unit to generate a set of extracted classification features, to select a subset of pivotal features from the set of classification features according to at least one correlation requirement between at least one classification variable and each respective member of the set of extracted features, and to store the selected subset of pivotal features in the storage unit for use in an automated machine learning process. 17. The system of claim 16, further comprising a user interface coupled to the processor, the user interface set to allow a user to input the at least one classification variable. 18. The system of claim 16, further comprising a user interface coupled to the processor, the user interface set to allow a user to select the at least one function from the set of functions. 19. The system of claim 16, further comprising code to train a statistical classifier for classification of the at least one classification variable based on the selected subset of pivotal features applied to the training dataset. 20. The system of claim 16, wherein the at least one correlation requirement includes a threshold for a predefined processing time for predicting the at least one classification variable using a target processing unit. 21. A computer program product comprising a non-transitory computer readable storage medium storing program code thereon for implementation by a processor of a system for generating features for use in an automated machine learning process, the program code comprising: instructions to receive a designated first training dataset comprising a plurality of raw data instances, each instance including a set of objects of arbitrary types, each object assigned at least one value, the raw data instances being unclassified; instructions to apply a function to each data instance to calculate a set of first results, the function being adapted to process at least one object of the set of objects of each instance, the function being adapted to process objects of arbitrary type; instructions to generate a set of classification features for application to a newly received data instance, each classification feature including the function for application to the newly received data instance to calculate a second result, and at least one condition defined by at least one respective member of the set of first results applied to the second result; instructions to apply each feature of the generated set of classification features to each instance of a second training dataset of unclassified raw data to generate a set of extracted features; instructions to select a subset of pivotal classification features from the set of classification features according to a correlation requirement between at least one classification variable and each respective member of the set of extracted features; and instructions to document the selected subset of pivotal features for use in an automated machine learning process.