METHOD AND SYSTEM FOR CREATING A PARAMETER OF QUALITY FORECAST FOR A FORECASTING MODEL PERFORMED IN A MACHINE LEARNING ALGORITHM Russian patent published in 2019 - IPC G06N20/00 G06N5/00 

FIELD: computer equipment.

SUBSTANCE: invention relates to the field of computer equipment. Method for determining the prediction quality parameter for a decision tree in a prognostic decision tree model is disclosed, this level of the decision tree has at least one node; a forecast quality parameter is used to assess the forecast quality of the predictive model of the decision tree at this iteration of learning the decision tree, method is performed by a machine learning system that performs a predictive model of the decision tree, the method includes: obtaining access from a permanent machine-readable carrier of a machine learning system, set of learning objects, each learning object from a set of learning objects includes an indication of a document and purpose associated with the document; organizing a set of learning objects into an ordered list of learning objects, the ordered list of learning objects being organized in such a way, that for each learning object in the ordered list of learning objects there is at least one of: (i) the previous learning object that is before the given learning object, and (ii) a subsequent training object, which is located after the given training object; descent of a set of learning objects on the decision tree in such a way that each of the set of learning objects is classified by the model of the decision tree at this iteration of learning to a given child node from at least one node at a given level of the decision tree; creating a forecast quality parameter for the decision tree by creating for this training object that was classified into this child node, forecast quality parameter, the creation is performed based on the goals of only those learning objects that are before the learning object in the ordered list of learning objects.

EFFECT: technical result is the determination of the forecast quality parameter for a decision tree in a predictive model of the decision tree.

30 cl, 13 dwg