Automated machine learning using nearest neighbor recommender systems

A computer program product comprising a computer readable storage medium having stored thereon:

• program instructions programmed to obtain a performance matrix representing accuracies obtained by executing a plurality of machine-learning pipelines on a plurality of training data sets, wherein a machine-learning pipeline comprises a series of operations performed on a data set;
• program instructions programmed to select a defined number of top machine-learning pipelines as potential machine-learning pipelines for a testing data set based on a similarity between the testing data set and each of the plurality of training data sets represented in the performance matrix;
• program instructions programmed to store results from executing each of the potential machine-learning pipelines as a new data set;
• program instructions programmed to determine a pipeline accuracy for each of the potential machine-learning pipelines when executed against the testing data set;
• and
• program instructions programmed to provide a recommended machine-learning pipeline for use with the testing data set based, at least in part, on the pipeline accuracy for each potential machine-learning pipeline.

A computer system comprising:

• a processor(s) set;
• and
• a computer readable storage medium;
• wherein:
• the processor set is structured, located, connected and/or programmed to run program instructions stored on the computer readable storage medium;
• and
• the stored program instructions include:
• program instructions programmed to obtain a performance matrix representing accuracies obtained by executing a plurality of machine-learning pipelines on a plurality of training data sets, wherein a machine-learning pipeline comprises a series of operations performed on a data set;
• program instructions programmed to select a defined number of top machine-learning pipelines as potential machine-learning pipelines for a testing data set based, at least in part, on similarities between the testing data set and each of the plurality of training data sets represented in the performance matrix;
• program instructions programmed to store results from executing each of the potential machine-learning pipelines as a new data set;
• program instructions programmed to determine a pipeline accuracy for each of the potential machine-learning pipelines when executed against the testing data set;
• and
• program instructions programmed to provide a recommended machine-learning pipeline for use with the testing data set based, at least in part, on the pipeline accuracy for each potential machine-learning pipeline.