Method of and server for converting categorical feature value into a numeric representation thereof and for generating a split value for the categorical feature

A server configured to execute a Machine Learning Algorithm (MLA), the MLA being based on a decision tree prediction model based on a decision tree, the decision tree being configured to process a categorical feature value by converting it into a numeric representation thereof, the categorical feature being associated with a training object used for training a Machine Learning Algorithm (MLA), the training object being processed in a node of a given level of the decision tree, the decision tree having at least one prior level of the decision tree, the at least one prior level having at least one prior training object having at least one categorical feature value having been converted to a prior numeric representation thereof for the at least one prior level of the decision tree, the server comprising:

• a non-transitory computer-readable medium;
• a processor coupled to the non-transitory computer-readable medium, the processor configured to:
• access, from a non-transitory computer-readable medium of the machine learning system, a set of training objects,
• each training object of the set of training object contains a document and an event indicator associated with the document, each document being associated with a categorical feature;
• generate the numeric representation of the categorical feature value by:
• retrieving the prior numeric representation of the at least one prior categorical feature value for a given object of the set of training objects at the at least one prior level of the decision tree;
• generating, for each combination of the at least one prior categorical feature value at the at least one prior level of the decision tree and at least some of the categorical features values of the set of training objects, a current numeric representation for the given level of the decision tree,
• the generating being done in-line with generating the decision tree.

A method of generating a split value for a node in a decision tree of a decision tree model used by a Machine Learning Algorithm (MLA), the split value being for a node at a particular level of the decision tree, the node for classifying an object having a categorical feature value that is to be translated into a numeric value representative thereof, the split is for causing the object to be classified in one of child nodes of the node based on the numeric value and the split value, the MLA executable by an electronic device to predict a value for an in-use object, the method comprising:

• generating a range of all possible values of the categorical features;
• applying a grid to the range to separate the range into region, each region having a boundary;
• using the boundary as the split value;
• the generating and the applying being executed before the categorical feature value is translated into the numeric representation thereof.