Abstract: A method comprises receiving a new data set; identifying at least one prior data set of a plurality of prior data sets that matches the new data set; generating a natural language data science problem statement for the new data set based on information associated with the at least prior one data set that matches the new data set; outputting the generated natural language data science problem statement for user verification; and in response to receiving user input verifying the natural language generated data science problem statement, generating one or more AutoAI configuration settings for the new data set based on one or more AutoAI configuration settings associated with the at least one prior data set that matches the new data set.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate visualization of a model selection process are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interaction backend handler component that obtains one or more assessment metrics of a model pipeline candidate. The computer executable components can further comprise a visualization render component that renders a progress visualization of the model pipeline candidate based on the one or more assessment metrics.

Abstract: A computer-implemented method according to one embodiment includes identifying a data set and meta information; and augmenting the data set with additional features in response to an automatic analysis of the data set in view of the meta information.

Abstract: Embodiments relate to a system, program product, and method for leveraging cognitive systems to facilitate the automated data table discovery for automated machine learning, and, more specifically, to leveraging a trained cognitive system to automatically search for additional data in an external data source that may be merged with an initial user-selected data table to generate a more robust machine learning model. Manual efforts to find and validate data appropriate for building and training a particular model for a particular task are significantly reduced. Specifically, a learning-based approach to leverage with machine learning models to automatically discover related datasets and join the datasets for a given initial dataset is disclosed herein. Operations that include dataset selection facilitate continued reinforcement learning of the systems.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate conditional parallel coordinates in automated artificial intelligence with constraints are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a visualization component that renders a pipeline constraint as a constraint axis having constraint scores of machine learning pipelines in a conditional parallel coordinates visualization. The computer executable components can further comprise a model generation component that generates a machine learning model based on the constraint scores of the machine learning pipelines.

Abstract: A dataset including features and values associated with the features can be received. Each of the features in the dataset can be mapped to a corresponding node in a knowledge graph based on the concept represented by the corresponding node. The knowledge graph can be traversed to find a candidate node connected to at least one mapped node, the candidate node not being mapped to a feature in the dataset. A concept associated with the candidate node can be identified as a new feature. A machine learning model pipeline can use the features in the dataset and the new feature to select a subset of features for training a machine learning model.

Abstract: A method comprises receiving a new data set; identifying at least one prior data set of a plurality of prior data sets that matches the new data set; generating a natural language data science problem statement for the new data set based on information associated with the at least prior one data set that matches the new data set; outputting the generated natural language data science problem statement for user verification; and in response to receiving user input verifying the natural language generated data science problem statement, generating one or more AutoAI configuration settings for the new data set based on one or more AutoAI configuration settings associated with the at least one prior data set that matches the new data set.

Abstract: Embodiments relate to a system, program product, and method for leveraging cognitive systems to facilitate the automated data table discovery for automated machine learning, and, more specifically, to leveraging a trained cognitive system to automatically search for additional data in an external data source that may be merged with an initial user-selected data table to generate a more robust machine learning model. Manual efforts to find and validate data appropriate for building and training a particular model for a particular task are significantly reduced. Specifically, a learning-based approach to leverage with machine learning models to automatically discover related datasets and join the datasets for a given initial dataset is disclosed herein. Operations that include dataset selection facilitate continued reinforcement learning of the systems.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate visualization of a model selection process are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interaction backend handler component that obtains one or more assessment metrics of a model pipeline candidate. The computer executable components can further comprise a visualization render component that renders a progress visualization of the model pipeline candidate based on the one or more assessment metrics.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate conditional parallel coordinates in automated artificial intelligence with constraints are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a visualization component that renders a pipeline constraint as a constraint axis having constraint scores of machine learning pipelines in a conditional parallel coordinates visualization. The computer executable components can further comprise a model generation component that generates a machine learning model based on the constraint scores of the machine learning pipelines.

Abstract: A conditional parallel coordinate visualization system is provided. The system presents a parallel coordinate visualization that includes a set of parallel main axes that respectively correspond to a set of main dimensions. The system receives a first multivariate data including values at the set of main dimensions. The first multivariate data has a first additional data that includes values in a first set of sub-dimensions. The first set of sub-dimensions is associated with a first predicate value at a first predicate dimension in the set of main dimensions. The system presents the first multivariate data as a polyline that intersects the set of parallel main axes. Upon a selection of an option item, the system unfolds the parallel coordinate visualization to reveal a first set of parallel sub-axes that correspond to the first set of sub-dimensions. The system presents the first additional data at the first set of parallel sub-axes.

Abstract: A conditional parallel coordinate visualization system is provided. The system presents a parallel coordinate visualization that includes a set of parallel main axes that respectively correspond to a set of main dimensions. The system receives a first multivariate data including values at the set of main dimensions. The first multivariate data has a first additional data that includes values in a first set of sub-dimensions. The first set of sub-dimensions is associated with a first predicate value at a first predicate dimension in the set of main dimensions. The system presents the first multivariate data as a polyline that intersects the set of parallel main axes. Upon a selection of an option item, the system unfolds the parallel coordinate visualization to reveal a first set of parallel sub-axes that correspond to the first set of sub-dimensions. The system presents the first additional data at the first set of parallel sub-axes.