首页|Study Results from Purdue University Provide New Insights into Machine Learning (Investigating Machine Learning’s Capacity To Enhance the Prediction of Career C hoices)
Study Results from Purdue University Provide New Insights into Machine Learning (Investigating Machine Learning’s Capacity To Enhance the Prediction of Career C hoices)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news originating from West Lafayette, Indiana, by N ewsRx correspondents, research stated, “Vocational interest measurement has long played a significant role in work contexts, particularly in helping individuals make career choices. A recent meta-analysis indicated that interest inventories have substantial validity for predicting career choices.” Our news journalists obtained a quote from the research from Purdue University, “However, traditional approaches to interest inventory scoring (e.g., profile ma tching) typically capture broad, or average relations between vocational interes ts and occupations in the population, yet may not be accurate in capturing the s pecific relations in a given sample. Machine learning (ML) approaches provide a potential way forward as they can effectively take into account complexities in the relation between interests and career choices. Thus, this study aims to enha nce the accuracy of interest inventory-based career choice prediction through th e application of ML. Using a large sample (N = 81,267) of employed and unemploye d participants,we compared the prediction accuracy of a traditional interest pr ofile method (profile matching) to a new machine-learning augmented method in pr edicting occupational membership (for employed participants) and vocational aspi rations (for unemployed participants). Results suggest that, compared to the tra ditional profile method, the machine-learning augmented method resulted in highe r overall accuracy for predicting both types of career choices. The machine-lear ning augmented method was especially predictive of job categories with high base rates, yet underpredicted job categories with low base rates.”
West LafayetteIndianaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningPur due University
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