Abstract: There is provided a method for training a ML model ensemble, comprising: training a central ML model using training values of central location parameters of a central location, for sample resources: training a respective ML model using the remote location parameters for the respective sample resource, receiving a manual indication of allocation of the respective sample resource to the central location or the remote location, creating a main training dataset including the training values for the central locations, and the training values for the remote location parameters for each of the sample resources labelled with the manual indication, and training a main ML model using the training dataset for allocating a resource to central location(s) or to the respective remote location, wherein a subset of resources are allocated to respective remote locations and another subset of the resources are allocated to central location(s).

Abstract: There is provided a method for training a ML model ensemble, comprising: training a central ML model using training values of central location parameters of a central location, for sample resources: training a respective ML model using the remote location parameters for the respective sample resource, receiving a manual indication of allocation of the respective sample resource to the central location or the remote location, creating a main training dataset including the training values for the central locations, and the training values for the remote location parameters for each of the sample resources labelled with the manual indication, and training a main ML model using the training dataset for allocating a resource to central location(s) or to the respective remote location, wherein a subset of resources are allocated to respective remote locations and another subset of the resources are allocated to central location(s).