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 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: There is disclosed a computer implemented method of generating a training object for training a machine learning algorithm (MLA). The method comprises: acquiring a digital training document to be used in the training; transmitting the digital training document to a plurality of assessors, transmitting further including indicating a range of possible labels for the assessors to assess from, the range of possible labels including at least a first possible label and a second possible label; obtaining from each of the plurality of assessors a selected label to form a pool of selected labels; generating a consensus label distribution based on the pool of selected labels, the consensus label distribution representing a range of perceived labels for the digital training document and an associated probability score for each of the perceived labels; and training the machine learning algorithm using the digital training document and the consensus label distribution.

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: There is disclosed a computer implemented method of generating a training object for training a machine learning algorithm (MLA). The method comprises: acquiring a digital training document to be used in the training; transmitting the digital training document to a plurality of assessors, transmitting further including indicating a range of possible labels for the assessors to assess from, the range of possible labels including at least a first possible label and a second possible label; obtaining from each of the plurality of assessors a selected label to form a pool of selected labels; generating a consensus label distribution based on the pool of selected labels, the consensus label distribution representing a range of perceived labels for the digital training document and an associated probability score for each of the perceived labels; and training the machine learning algorithm using the digital training document and the consensus label distribution.

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.