Interactive machine learning optimization
Methods, computer program products, and systems are presented. The method, computer program products, and systems can include, for instance: examining an enterprise dataset, the enterprise dataset defined by enterprise collected data; selecting one or more synthetic dataset in dependence on the examining, the one or more synthetic dataset including data other than data collected by the enterprise; training a set of predictive models using data of the one or more synthetic dataset to provide a set of trained predictive models; testing the set of trained predictive models with use of holdout data of the one or more synthetic dataset; and presenting prompting data on a displayed user interface of a developer user in dependence on result data resulting from the testing, the prompting data prompting the developer user to direct action with respect to one or more model of the set of predictive models.
1 . A computer implemented method comprising:
examining an enterprise dataset, the enterprise dataset defined by enterprise collected data;
selecting one or more synthetic dataset in dependence on the examining, the one or more synthetic dataset including data other than data collected by the enterprise, wherein the examining the enterprise dataset includes subjecting the enterprise dataset to processing for extraction of enterprise dataset characterizing parameter values and comparing the enterprise dataset characterizing parameter values to respective sets of synthetic dataset characterizing parameter values stored in a data repository, wherein the respective sets of synthetic dataset characterizing parameter values characterize respective synthetic datasets stored in the data repository, wherein the selecting one or more synthetic dataset in dependence on the examining includes identifying from the comparing at least one synthetic dataset stored in the data repository having a threshold satisfying similarity with the enterprise dataset and identifying from the respective synthetic datasets stored in the data repository a highest ranked synthetic dataset having a greatest similarity to the enterprise dataset amongst the respective synthetic datasets stored in the data repository, and wherein the selecting one or more synthetic dataset in dependence on the examining includes performing the selecting so that the selected one or more synthetic dataset have the threshold satisfying similarity with the enterprise dataset and include the highest ranked synthetic dataset having the greatest similarity to the enterprise dataset amongst the respective synthetic datasets stored in the data repository;
training a set of predictive models using data of the selected one or more synthetic dataset having the threshold satisfying similarity with the enterprise dataset and including the highest ranked synthetic dataset having the greatest similarity to the enterprise dataset amongst the respective synthetic datasets stored in the data repository to provide a set of trained predictive models;
testing the set of trained predictive models with use of holdout data of the one or more synthetic dataset; and
presenting prompting data on a displayed user interface of a developer user in dependence on result data resulting from the testing, the prompting data prompting the developer user to direct action with respect to one or more model of the set of predictive models, wherein the prompting data prompting the developer user to direct action with respect to one or more model of the set of predictive models includes prompting data prompting the developer user to select, in dependence on data of the result data, a certain synthetic dataset for training and testing a certain predictive model of the set of predictive models, wherein the user interface guides the developer user by presenting a recommended datasets text area displaying recommended synthetic datasets including a highlighted dataset indicator distinguishing a recommended dataset from other candidate datasets, and restricts the developer user from selecting an unrecommended dataset for a predictive model such that, during a subsequent iteration, different predictive models are trained on differentiated synthetic datasets.
2 . The method of claim 1 , wherein testing the set of trained predictive models includes generating a ranked listing of model specific performance metrics, and wherein the user interface presents a model identifier text area that displays the ranked listing to visually correlate each predictive model with its respective performance metric.
3 . The method of claim 1 , wherein the user interface presents a results visualization area that plots predicted signals produced by multiple predictive models alongside a ground truth signal to enable qualitative comparison of model behavior over a shared time axis.
4 . The method of claim 1 , wherein the user interface presents a model attributes text area that displays attributes including model type, capacity, and training iteration count, and dynamically updates the displayed attributes in dependence on testing result data.
5 . The method of claim 1 , wherein the presenting prompting data includes displaying classifications of candidate synthetic datasets by attributes including seasonality, trend, variance, outliers, or level shift, and wherein the classifications are generated by processing metadata of the respective synthetic datasets stored in the data repository.
6 . The method of claim 1 , wherein the presenting prompting data includes displaying integrated visual guidance that correlates model specific error signals with dataset selection information derived from the testing, enabling the developer user to interpret relationships between error behavior and training data characteristics.
7 . The method of claim 1 , wherein the presenting prompting data includes querying a machine learning trained model that is trained to predict a next dataset for training a predictive model of the set of predictive models, wherein the trained model is configured using crowdsourced developer action data identifying historical developer selections of synthetic datasets.
8 . A system comprising:
a memory;
at least one processor in communication with the memory; and
program instructions executable by one or more processor via the memory to perform a method comprising:
examining an enterprise dataset, the enterprise dataset defined by enterprise collected data;
selecting one or more synthetic dataset in dependence on the examining, the one or more synthetic dataset including data other than data collected by the enterprise, wherein the examining the enterprise dataset includes subjecting the enterprise dataset to processing for extraction of enterprise dataset characterizing parameter values and comparing the enterprise dataset characterizing parameter values to respective sets of synthetic dataset characterizing parameter values stored in a data repository, wherein the respective sets of synthetic dataset characterizing parameter values characterize respective synthetic datasets stored in the data repository, wherein the selecting one or more synthetic dataset in dependence on the examining includes identifying from the comparing at least one synthetic dataset stored in the data repository having a threshold satisfying similarity with the enterprise dataset and identifying from the respective synthetic datasets stored in the data repository a highest ranked synthetic dataset having a greatest similarity to the enterprise dataset amongst the respective synthetic datasets stored in the data repository, and wherein the selecting one or more synthetic dataset in dependence on the examining includes performing the selecting so that the selected one or more synthetic dataset have the threshold satisfying similarity with the enterprise dataset and include the highest ranked synthetic dataset having the greatest similarity to the enterprise dataset amongst the respective synthetic datasets stored in the data repository;
training a set of predictive models using data of the selected one or more synthetic dataset having the threshold satisfying similarity with the enterprise dataset and including the highest ranked synthetic dataset having the greatest similarity to the enterprise dataset amongst the respective synthetic datasets stored in the data repository to provide a set of trained predictive models;
testing the set of trained predictive models with use of holdout data of the one or more synthetic dataset; and
presenting prompting data on a displayed user interface of a developer user in dependence on result data resulting from the testing, the prompting data prompting the developer user to direct action with respect to one or more model of the set of predictive models, wherein the prompting data prompting the developer user to direct action with respect to one or more model of the set of predictive models includes prompting data prompting the developer user to select, in dependence on data of the result data, a certain synthetic dataset for training and testing a certain predictive model of the set of predictive models, wherein the user interface guides the developer user by presenting a recommended datasets text area displaying recommended synthetic datasets including a highlighted dataset indicator distinguishing a recommended dataset from other candidate datasets, and restricts the developer user from selecting an unrecommended dataset for a predictive model such that, during a subsequent iteration, different predictive models are trained on differentiated synthetic datasets.
9 . The system of claim 8 , wherein testing the set of trained predictive models includes generating a ranked listing of model specific performance metrics, and wherein the user interface presents a model identifier text area that displays the ranked listing to visually correlate each predictive model with its respective performance metric.
10 . The system of claim 8 , wherein the user interface presents a results visualization area that plots predicted signals produced by multiple predictive models alongside a ground truth signal to enable qualitative comparison of model behavior over a shared time axis.
11 . The system of claim 8 , wherein the user interface presents a model attributes text area that displays attributes including model type, capacity, and training iteration count, and dynamically updates the displayed attributes in dependence on testing result data.
12 . The system of claim 8 , wherein the presenting prompting data includes displaying classifications of candidate synthetic datasets by attributes including seasonality, trend, variance, outliers, or level shift, and wherein the classifications are generated by processing metadata of the respective synthetic datasets stored in the data repository.
13 . The system of claim 8 , wherein the presenting prompting data includes displaying integrated visual guidance that correlates model specific error signals with dataset selection information derived from the testing, enabling the developer user to interpret relationships between error behavior and training data characteristics.
14 . The system of claim 8 , wherein the presenting prompting data includes querying a machine learning trained model that is trained to predict a next dataset for training a predictive model of the set of predictive models, wherein the trained model is configured using crowdsourced developer action data identifying historical developer selections of synthetic datasets.
15 . A computer program product comprising:
a computer readable storage medium readable by one or more processing circuit and storing instructions for execution by one or more processor for performing a method comprising:
examining an enterprise dataset, the enterprise dataset defined by enterprise collected data;
selecting one or more synthetic dataset in dependence on the examining, the one or more synthetic dataset including data other than data collected by the enterprise, wherein the examining the enterprise dataset includes subjecting the enterprise dataset to processing for extraction of enterprise dataset characterizing parameter values and comparing the enterprise dataset characterizing parameter values to respective sets of synthetic dataset characterizing parameter values stored in a data repository, wherein the respective sets of synthetic dataset characterizing parameter values characterize respective synthetic datasets stored in the data repository, wherein the selecting one or more synthetic dataset in dependence on the examining includes identifying from the comparing at least one synthetic dataset stored in the data repository having a threshold satisfying similarity with the enterprise dataset and identifying from the respective synthetic datasets stored in the data repository a highest ranked synthetic dataset having a greatest similarity to the enterprise dataset amongst the respective synthetic datasets stored in the data repository, and wherein the selecting one or more synthetic dataset in dependence on the examining includes performing the selecting so that the selected one or more synthetic dataset have the threshold satisfying similarity with the enterprise dataset and include the highest ranked synthetic dataset having the greatest similarity to the enterprise dataset amongst the respective synthetic datasets stored in the data repository;
training a set of predictive models using data of the selected one or more synthetic dataset having the threshold satisfying similarity with the enterprise dataset and including the highest ranked synthetic dataset having the greatest similarity to the enterprise dataset amongst the respective synthetic datasets stored in the data repository to provide a set of trained predictive models;
testing the set of trained predictive models with use of holdout data of the one or more synthetic dataset; and
presenting prompting data on a displayed user interface of a developer user in dependence on result data resulting from the testing, the prompting data prompting the developer user to direct action with respect to one or more model of the set of predictive models, wherein the prompting data prompting the developer user to direct action with respect to one or more model of the set of predictive models includes prompting data prompting the developer user to select, in dependence on data of the result data, a certain synthetic dataset for training and testing a certain predictive model of the set of predictive models, wherein the user interface guides the developer user by presenting a recommended datasets text area displaying recommended synthetic datasets including a highlighted dataset indicator distinguishing a recommended dataset from other candidate datasets, and restricts the developer user from selecting an unrecommended dataset for a predictive model such that, during a subsequent iteration, different predictive models are trained on differentiated synthetic datasets.
16 . The computer program product of claim 15 , wherein testing the set of trained predictive models includes generating a ranked listing of model specific performance metrics, and wherein the user interface presents a model identifier text area that displays the ranked listing to visually correlate each predictive model with its respective performance metric.
17 . The computer program product of claim 15 , wherein the user interface presents a results visualization area that plots predicted signals produced by multiple predictive models alongside a ground truth signal to enable qualitative comparison of model behavior over a shared time axis.
18 . The computer program product of claim 15 , wherein the user interface presents a model attributes text area that displays attributes including model type, capacity, and training iteration count, and dynamically updates the displayed attributes in dependence on testing result data.
19 . The computer program product of claim 15 , wherein the presenting prompting data includes displaying integrated visual guidance that correlates model specific error signals with dataset selection information derived from the testing, enabling the developer user to interpret relationships between error behavior and training data characteristics.
20 . The computer program product of claim 15 , wherein the presenting prompting data includes querying a machine learning trained model that is trained to predict a next dataset for training a predictive model of the set of predictive models, wherein the trained model is configured using crowdsourced developer action data identifying historical developer selections of synthetic datasets.