Guiding machine learning models and related components

A computer-implemented method comprising:

• receiving, by a device operatively coupled to a processor, a user query for suggested models, wherein the user query comprises model-related data;
• identifying, by the device, models, wherein the models respectively comprise model components;
• training, by the device, a cognitive exploration system comprising a neural network to determine model relations among the model components and the model-related data;
• determining, by the device, using the cognitive exploration system comprising the neural network, the model relations among the model components and the model-related data, wherein the model relations comprise respective vectors of component relations between respective pairwise ones of the model components and the model-related data;
• and
• responding, by the device, to the user query with the suggested models comprising a subset of the models selected, using the cognitive exploration system comprising the neural network, based on the vectors of component relations, wherein the subset of the models are selected to have a high dissimilarity to the model-related data according to a tolerance parameter indicative of degree of similarity.

A computer program product for providing guidance in machine learning models, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• receive a user query for suggested models, wherein the user query comprises model-related data;
• identify models, wherein the models respectively comprise model components;
• train a cognitive exploration system comprising a neural network to determine model relations among the model components and the model-related data;
• determine, using the cognitive exploration system comprising the neural network, the model relations among the model components and the model-related data, wherein the model relations comprise respective vectors of component relations between respective pairwise ones of the model components and the model-related data;
• and
• respond to the user query with the suggested models comprising a subset of the models selected, using the cognitive exploration system comprising the neural network, based on the vectors of component relations, wherein the subset of the models are selected to be of greater dissimilarity to the model-related data according to a tolerance parameter indicative of degree of similarity.

A system comprising:

• a memory that stores computer executable components;
• and
• a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
• a suggestion component that receives a user request for suggested models, wherein the user request comprises model-related data, and the model-related data comprises a training data set and specifies a type of problem to be solved;
• an identification component that identifies models, wherein the models respectively comprise model components;
• a learning component trains a neural network to determine model relations among the model components and the model-related data;
• a determination component that determines, using the neural network, the model relations among the model components and the model-related data, wherein the model relations comprise vectors of component relations between respective pairwise ones of the model components and the model-related data;
• and
• the suggestion component responds to the user request with the suggested models comprising a subset of the models that have been previously employed for the type of problem to be solved and are selected, using the neural network, based on the vectors of component relations, wherein the subset of the models are selected to be most dissimilar to the model-related data according to a tolerance parameter indicative of degree of similarity.

A computer-implemented method comprising:

• receiving, by a device operatively coupled to a processor, a user request for suggested models, wherein the user request comprises model-related data, and the model-related data comprises a training data set and specifies a type of problem to be solved;
• identifying, by the device, models, wherein the models respectively comprise model components;
• training, by the device, a neural network to determine model relations among the model components and the model-related data;
• determining, by the device, using the neural network, the model relations among the model components and the model-related data, wherein the model relations comprise respective vectors of component relations between respective pairwise ones of the model components and the model-related data;
• and
• responding, by the device, to the user request with the suggested models comprising a subset of the models that have been previously employed for the type of problem to be solved and are selected, using the neural network, based on the vectors of component relations, wherein the subset of the models are selected to have a high dissimilarity to the model-related data according to a tolerance parameter indicative of degree of similarity.