Abstract: A computer-implemented method of and a system for generating a prediction model and determining an accuracy parameter of a trained decision tree prediction model. The method comprises accessing the trained decision tree prediction model having been generated at least partially based on a set of training objects; generating a subset of random parameters of interest; associating the subset of random parameters of interests with a given leaf; determining a leaf accuracy parameter based on (i) the parameters of interest associated with the given leaf and (ii) the subset of random parameters of interest of the given leaf; and determining the accuracy parameter of the trained decision tree prediction model based on the determined leaf accuracy parameter for each of the leafs of the decision tree.

Abstract: There is disclosed a method of and a system for converting a categorical feature value into a numeric representation thereof, the categorical feature being associated with a training object used for training a Machine Learning Algorithm (MLA). The MLA is trained using several models, each model comprising a plurality of decision trees (an ensemble of decision trees). For each model, a respective set of training objects into an ordered list of training objects such that for each given training object 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. The method further comprises, for a given categorical feature, using a respective ordered set to generate numeric representation of values of the given categorical feature.

Abstract: There is disclosed a method of determining an optimal value of an auction parameter for a digital object. The method comprises using: an indication of a digital object, an auction parameter associated with the digital object and an environment feature at the respective moment of time to execute an offline training of a machine learning algorithm to predict an optimal value of auction parameters for a plurality of digital objects, the plurality of digital objects being associated with the interaction history of the first portion of users. The method further comprises applying the machine learning algorithm to determine a first optimal value of an auction parameter for a plurality of digital objects associated with the second portion of users and using such determined value for determining a digital object being relevant to the request from a user from the second portion of users.

Abstract: A computer-implemented method of and a system for generating a prediction model and determining an accuracy parameter of a trained decision tree prediction model. The method comprises accessing the trained decision tree prediction model having been generated at least partially based on a set of training objects; generating a subset of random parameters of interest; associating the subset of random parameters of interests with a given leaf; determining a leaf accuracy parameter based on (i) the parameters of interest associated with the given leaf and (ii) the subset of random parameters of interest of the given leaf; and determining the accuracy parameter of the trained decision tree prediction model based on the determined leaf accuracy parameter for each of the leafs of the decision tree.

Abstract: A computer-implemented method of and a system for generating a prediction model and determining an accuracy parameter of a trained decision tree prediction model. The method comprises accessing the trained decision tree prediction model having been generated at least partially based on a set of training objects; generating a subset of random parameters of interest; associating the subset of random parameters of interests with a given leaf; determining a leaf accuracy parameter based on (i) the parameters of interest associated with the given leaf and (ii) the subset of random parameters of interest of the given leaf; and determining the accuracy parameter of the trained decision tree prediction model based on the determined leaf accuracy parameter.

Abstract: A method of conducting a search is executable at a server communicatively coupled to an electronic device via a communication network. The method includes receiving, via the communication network, a search query from the electronic device, and responsive thereto, determining a search query relevant host including a plurality of resources related to at least a portion of the search query. A search query relevant resource address associated with a search query relevant resource is generated. The search query relevant resource is relevant to the search query. The search query relevant resource address is based on an address template associated with the search query relevant host and at least a portion of the search query. The electronic device is caused to display a search engine results page (SERP) including a search result indicative of at least one of: the search query relevant resource and the search query relevant resource address.

Abstract: There is disclosed a method of and a system for converting a categorical feature value into a numeric representation thereof, the categorical feature being associated with a training object used for training a Machine Learning Algorithm (MLA). The MLA is trained using several models, each model comprising a plurality of decision trees (an ensemble of decision trees). For each model, a respective set of training objects into an ordered list of training objects such that for each given training object 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. The method further comprises, for a given categorical feature, using a respective ordered set to generate numeric representation of values of the given categorical feature.

Abstract: A method of determining a prediction quality parameter for a decision tree in a decision tree prediction model for evaluating prediction quality of the decision tree prediction model. The method comprises organizing the set of training objects into an ordered list of training objects 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. The method further comprises 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 parameter based on targets of only those training objects that occur before the given training object.

Abstract: There is disclosed a method of and a system for training and using a Machine Learning Algorithm (MLA), the MLA using a decision tree model having a decision tree. During training a training object being associated with a categorical feature and is processed at a node of the decision tree. The method comprises calculating a numeric representation of the categorical feature and the value of the splits for the node “in-line” with generating a given iteration of the decision tree.

Abstract: There is disclosed a method of determining an optimal value of an auction parameter for a digital object. The method comprises using: an indication of a digital object, an auction parameter associated with the digital object and an environment feature at the respective moment of time to execute an offline training of a machine learning algorithm to predict an optimal value of auction parameters for a plurality of digital objects, the plurality of digital objects being associated with the interaction history of the first portion of users. The method further comprises applying the machine learning algorithm to determine a first optimal value of an auction parameter for a plurality of digital objects associated with the second portion of users and using such determined value for determining a digital object being relevant to the request from a user from the second portion of users.

Abstract: A computer-implemented method of and a system for generating a prediction model and determining an accuracy parameter of a trained decision tree prediction model. The method comprises accessing the trained decision tree prediction model having been generated at least partially based on a set of training objects; generating a subset of random parameters of interest; associating the subset of random parameters of interests with a given leaf; determining a leaf accuracy parameter based on (i) the parameters of interest associated with the given leaf and (ii) the subset of random parameters of interest of the given leaf; and determining the accuracy parameter of the trained decision tree prediction model based on the determined leaf accuracy parameter.

Abstract: A method of conducting a search is executable at a server communicatively coupled to an electronic device via a communication network. The method includes receiving, via the communication network, a search query from the electronic device, and responsive thereto, determining a search query relevant host including a plurality of resources related to at least a portion of the search query. A search query relevant resource address associated with a search query relevant resource is generated. The search query relevant resource is relevant to the search query. The search query relevant resource address is based on an address template associated with the search query relevant host and at least a portion of the search query. The electronic device is caused to display a search engine results page (SERP) including a search result indicative of at least one of: the search query relevant resource and the search query relevant resource address.