Method, device and computer program for predicting a suitable configuration of a machine learning system for a training data set

A method for predicting a suitable configuration of a machine learning system and/or a training method, for a first training data set, comprising the following steps, which are carried out on a computer:

• training a plurality of machine learning systems using the first training data set, the machine learning systems and/or training methods used for the training being configured differently;
• creating a second training data set which includes ascertained respective performance capabilities of the trained machine learning systems on the first training data set and respective assigned configurations;
• training a differentiable graph pooling network (DiffPool) or XGBoost or LGBoost, depending on the second training data set, so that the DiffPool or XGBoost or LGBoost ascertains the respective performance capabilities, depending on the respective assigned configurations;
• predicting performance capabilities for a provided plurality of configurations using the DiffPool or XGBoost or LGBoost;
• and
• selecting a configuration from the plurality for configurations for which a best performance capability was predicted.

A non-transitory machine-readable memory element on which is stored a computer program for predicting a suitable configuration of a machine learning system and/or a training method, for a first training data set, the computer program, when executed by a computer, causing the computer to perform the following steps:

• training a plurality of machine learning systems using the first training data set, the machine learning systems and/or training methods used for the training being configured differently;
• creating a second training data set which includes ascertained respective performance capabilities of the trained machine learning systems on the first training data set and respective assigned configurations;
• training a graph isomorphism network, depending on the second training data set, so that the graph isomorphism network ascertains the respective performance capabilities, depending on the respective assigned configurations;
• predicting performance capabilities for a provided plurality of configurations using the graph isomorphism network;
• and
• selecting a configuration from the provided plurality for configurations for which a best performance capability was predicted.

A device configured to predict a suitable configuration of a machine learning system and/or a training method, for a first training data set, the device configured to:

• train a plurality of machine learning systems using the first training data set, the machine learning systems and/or training methods used for the training being configured differently;
• create a second training data set which includes ascertained respective performance capabilities of the trained machine learning systems on the first training data set and respective assigned configurations;
• train a graph isomorphism network, depending on the second training data set, so that the graph isomorphism network ascertains the respective performance capabilities, depending on the respective assigned configurations;
• predict performance capabilities for a provided plurality of configurations using the graph isomorphism network;
• and
• select a configuration from the provided plurality for configurations for which a best performance capability was predicted.