Method of and system for generating prediction quality parameter for a prediction model executed in a machine learning algorithm

A method of determining a prediction quality parameter for a decision tree prediction model,

• a given level of the decision tree having at least one node,
• the prediction quality parameter being for evaluating prediction quality of the decision tree prediction model at a given iteration of training of the decision tree, the given iteration of training of the decision tree having at least one previous iteration of training of a previous decision tree, the decision tree and the previous decision tree forming an ensemble of tree generated using a decision tree boosting technique,
• the method being executable at a machine learning system that executes the decision tree prediction model,
• the method comprising:
• accessing, 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 containing an indication of a document and a target associated with the document;
• organizing the set of training objects into an ordered list of training objects, the ordered list of training objects is organized such that for each given training object in the ordered list of training objects there is at least one of:
• (i) a preceding training object that occurs before the given training object and
• (ii) a subsequent training object that occurs after the given training object;
• descending the set of training objects through the decision tree so that each one of the set of training objects gets categorized, by the decision tree model at the given iteration of training, into a given child node of the at least one node of the given level of the decision tree;
• generating the prediction quality parameter for the given level of a decision tree by:
• generating, for a given training object that has been categorized into the given child node, a prediction quality approximation parameter, the generating being executed based on:
• targets of only those training objects that occur before the given training object in the ordered list of training objects;
• and
• at least one prediction quality approximation parameter of the given training object generated during the previous iteration of the training of the previous decision tree.

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, a given level of the decision tree having at least one node, the server further configured to:

• access, from a non-transitory computer-readable medium of the server, a set of training objects, each training object of the set of training object containing an indication of a document and a target associated with the document;
• organize the set of training objects into an ordered list of training objects, the ordered list of training objects is organized such that for each given training object in the ordered list of training objects there is at least one of:
• (i) a preceding training object that occurs before the given training object and
• (ii) a subsequent training object that occurs after the given training object;
• descend the set of training objects through the decision tree so that each one of the set of training objects gets categorized, by the decision tree prediction model at the given iteration of training, into a given child node of the at least one node of the given level of the decision tree;
• generate a prediction quality parameter for the given level of a decision tree, the prediction quality parameter being for evaluating prediction quality of the decision tree prediction model at a given iteration of training of the decision tree, by:
• generating, for a given training object that has been categorized into the given child node, a prediction quality parameter, the generating being executed based on targets of only those training objects that occur before the given training object in the ordered list of training objects.

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, a given level of the decision tree having at least one node, the server further 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 containing an indication of a document and a target associated with the document;
• organize the set of training objects into an ordered list of training objects, the ordered list of training objects is organized such that for each given training object in the ordered list of training objects there is at least one of:
• (i) a preceding training object that occurs before the given training object and
• (ii) a subsequent training object that occurs after the given training object;
• descend the set of training objects through the decision tree so that each one of the set of training objects gets categorized, by the decision tree model at the given iteration of training, into a given child node of the at least one node of the given level of the decision tree;
• generate a prediction quality parameter for the given level of a decision tree, the prediction quality parameter being for evaluating prediction quality of the decision tree prediction model at a given iteration of training of the decision tree, the given iteration of training of the decision tree having at least one previous iteration of training of a previous decision tree, the decision tree and the previous decision tree forming an ensemble of tree generated using a decision tree boosting technique, by:
• generating, for a given training object that has been categorized into the given child node, a prediction quality approximation parameter, the generating being executed based on:
• targets of only those training objects that occur before the given training object in the ordered list of training objects;
• and
• at least one prediction quality approximation parameter of the given training object generated during the previous iteration of the training of the previous decision tree.

A method of determining a prediction quality parameter for a decision tree prediction model,

• a given level of the decision tree having at least one node,
• the prediction quality parameter being for evaluating prediction quality of the decision tree prediction model at a given iteration of training of the decision tree, the given iteration of training of the decision tree having at least one previous iteration of training of a previous decision tree, the decision tree and the previous decision tree forming an ensemble of tree generated using a decision tree boosting technique,
• the method being executable at a machine learning system that executes the decision tree prediction model,
• the method comprising:
• accessing, 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 containing an indication of a document and a target associated with the document;
• organizing the set of training objects into an ordered list of training objects, the ordered list of training objects is organized such that for each given training object in the ordered list of training objects there is at least one of:
• (i) a preceding training object that occurs before the given training object and
• (ii) a subsequent training object that occurs after the given training object;
• descending the set of training objects through the decision tree so that each one of the set of training objects gets categorized, by the decision tree model at the given iteration of training, into a given child node of the at least one node of the given level of the decision tree;
• generating the prediction quality parameter for the given level of a decision tree by:
• generating, for a given training object that has been categorized into the given child node, a prediction quality approximation parameter, the generating being executed based on:
• targets of only those training objects that occur before the given training object in the ordered list of training objects;
• and
• at least one prediction quality approximation parameter of the given training object generated during the previous iteration of the training of the previous decision tree;
• calculating an indication of the at least one quality approximation parameter of the given training object generated during the at least one previous iteration of the training of the previous decision tree by splitting the ordered list of training objects into a plurality of chunks, the plurality of chunks being organized into at least two levels of chunks.