摘要
由一名新闻记者兼机器人与机器学习的新闻编辑每日新闻-调查人员发布了关于马学习的新报告。《来自印第安纳州西拉斐特的新闻》由NEWSRX记者报道，研究表明：“职业兴趣测量长期以来在工作环境中发挥着重要作用，特别是在帮助个人做出职业选择方面。最近的一项荟萃分析表明，兴趣清单在预测职业选择方面具有很大的有效性。”我们的新闻记者从普渡大学的研究中获得了一句话，"然而，传统的兴趣清单评分方法（例如，个人资料）通常捕捉到职业兴趣和人口中职业之间广泛或平均的关系。机器学习(ML)方法能够有效地考虑到兴趣和职业选择之间关系的复杂性,因此,本研究旨在通过大量样本(N=81267)的应用,提高基于兴趣清单的职业选择预测的准确性。我们比较了传统兴趣描述法(轮廓匹配法)和新的机器学习增强法(针对就业参与者)在预测职业成员资格和职业ASPI比率(针对失业参与者)方面的预测精度。结果表明,与传统兴趣描述法相比,传统兴趣描述法(轮廓匹配法)和新的机器学习增强法在预测职业成员资格和职业ASPI比率方面的预测精度更高。机器学习增强方法在预测这两种类型的职业选择方面具有较高的总体准确性。机器学习增强方法特别能预测基础率高的工作类别，但对基础率低的工作类别预测不足。
Abstract
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.”
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