izvorni znanstveni rad
Paper shows solution for predictive model development in situation where rare and disruptive events bring most of the prediction. Such situation is mostly present in customer satisfaction prediction models where satisfaction depends on continuous delivery of products and services, where oscillation in quality of delivery or some events can ruin customer experience. Traditional approach which is oriented on machine learning models usage with usage of neural networks, logistic regression or similar methods are concentrated on statistically significant patterns and their combination. Oscillation in quality in delivery in modern business tends to be as lower as possible, which means that such events and disruptive events are potentially statistically insignificant, but when they happen, they have great influence on customer satisfaction. Pure machine learning approach hardly can recognize such situation with statistically insignificant events which has great influence on result. Paper will give solution for such business problems as well authored proposal for attribute relevance analysis for rare events.
Data science, rare events, neural networks, logistic regression, disruptive events
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