METHOD AND A SERVER FOR CONVERTING A CATEGORICAL FACTOR VALUE INTO ITS NUMERICAL REPRESENTATION AND FOR CREATING A SEPARATING VALUE OF A CATEGORICAL FACTOR

摘要

FIELD: computer equipment.;SUBSTANCE: invention relates to computer engineering. Disclosed is a method of converting into a numerical representation a categorical factor value which is associated with a training object for training machine learning algorithm (MLA), wherein MLA uses a decision tree model having a decision tree, wherein the training object is processed on a node of a given decision tree level, wherein the decision tree has at least one previous decision tree level, wherein at least one previous level value of at least one categorical factor is converted into its previous numerical representation for at least one previous level of decision tree, wherein machine learning algorithm is performed by electronic device for prediction of use phase object, method includes: obtaining access from a side of machine-readable carrier of machine learning system to a set of training objects, wherein each training object from a set of training objects comprises a document and an event indicator associated with the document, wherein each document is associated with a categorical factor; creating a numerical representation for a categorical factor value by extracting a previous numerical representation of at least one categorical factor value for the given object from the set of training entities on at least one previous decision tree level; creating, for each combination of at least one previous value of categorical factor on at least one previous level of decision tree and at least some values of categorical factors from set of training objects, current numerical representation for given level of decision tree, creation is carried out during creation of decision tree.;EFFECT: forming a machine learning algorithm using a decision tree model and designed to classify objects having a categorical factor value which is converted to its numerical representation.;42 cl, 14 dwg