THE EFFECT OF THE CONTROL SAMPLE VOLUME ON THE QUALITY OF DIAGNOSTICS OF THE TECHNICAL OBJECT STATE
He problem of predicting the serviceability of a technical object in terms of its performance is considered. The source data are the known results of the evaluation of the state of an object based on the results of the previous operation: If the specified values of controlled indicators technical system intact or defective. Such a problem can be solved by methods of machine learning, it reduces to a binary classification of the states of the object. The quality of diagnostics can significantly depend on many factors: the method of training, the correct allocation of factors characterizing the operation of the object, the volume of the sample, and others. The work studies the effect of the control sample volume on the quality of diagnosis, estimated by the number of mis-predicted states using the cross-validation method. The tests were carried out in the Matlab package, ten different training methods were used: logistic regression, support vector method, decision tree bugging, and others. It is shown that the correct choice of the proportion of the control sample can improve the diagnostic quality to 5-7%.